Financial auditing method and related device

Abstract

The invention relates to the field of data processing, in particular to a financial auditing method and a related device. The method comprises the steps of obtaining to-be-processed target financial data from a plurality of financial data sources; grouping the target financial data according to respective corresponding data attributes to obtain multi-dimensional data groups; verifying the multi-dimensional data groups from different dimensions through a data proofreading model to obtain a verification result of the target financial data; the data proofreading model is pre-configured with data proofreading strategies corresponding to different dimensions; performing compliance and risk analysis on the target financial data through a multi-dimensional analysis model to obtain an analysis result of the target financial data; and generating an audit report corresponding to the target financial data based on the verification result and the analysis result. According to the method, the automation degree, data proofreading efficiency and reliability of financial auditing can be improved through the data proofreading model and the multi-dimensional analysis model, and the method is suitable for various auditing scenes.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a financial auditing method and related devices. Background Art

[0002] Financial audit is an independent economic supervision activity, which aims to review and evaluate the authenticity, legality and effectiveness of the financial statements, financial revenue and expenditure and related economic activities of the audited unit. With the development of computer technology, more and more enterprises and institutions adopt financial data processing systems to improve the efficiency and accuracy of financial accounting audits and reduce audit costs.

[0003] However, in the related technologies, financial data processing systems are mostly focused on basic data recording, simple statistics, and manual verification functions. For example, for the auxiliary processing of a single process, such as bill auditing, contract auditing, and other single business processing links, other processes still require professionals to operate and complete audit reports based on their own experience. Therefore, the related technologies still have technical problems such as low automation in the audit process, low data processing efficiency, high difficulty in operation, easy to introduce errors due to improper manual operation, and difficult to ensure data quality. They cannot meet the demand for accelerated audit processes, nor can they adapt to flexible and changeable audit scenarios.

[0004] Therefore, it is urgent to propose a new technical solution to solve at least one of the above technical problems. Summary of the invention

[0005] In view of the technical problems existing in the prior art, this application provides a financial audit method and related devices, which are used to improve the automation level and audit efficiency of financial audits through data proofreading models and multidimensional analysis models, and adapt to various audit scenarios. In addition, the verification process is significantly accelerated, the operation steps are simplified, the operation difficulty is reduced, and the data proofreading efficiency and reliability are further improved. In addition, multi-dimensional comparison and trend analysis are performed to achieve in-depth review of financial data and ensure the accuracy and compliance of the data.

[0006] In a first aspect, an embodiment of the present application provides a financial audit method, the method comprising:

[0007] Acquire the target financial data to be processed from multiple financial data sources; the multiple financial data sources at least include: the financial data system in the target unit and the financial data system of the external cooperation unit;

[0008] Grouping the target financial data according to their corresponding data attributes to obtain multidimensional data groups;

[0009] Verifying the multidimensional data grouping from different dimensions through the data verification model to obtain verification results of the target financial data; the verification results at least include: rule verification results of each data item in the target financial data, abnormal data items in the target financial data that fail to pass the rule verification, and / or warning information corresponding to the abnormal data items; the data verification model is pre-configured with data verification strategies corresponding to different dimensions;

[0010] By using a multidimensional analysis model, compliance and risk analysis is performed on the target financial data to obtain analysis results of the target financial data; the analysis results include: at least one of a change trend between the target financial data, abnormal trend risk, financial indicator analysis, and abnormal fluctuation warning information;

[0011] Based on the verification results and the analysis results, an audit report corresponding to the target financial data is generated; the audit report at least includes: the verification results of the target financial data, compliance assessment, and risk warnings.

[0012] In a second aspect, an embodiment of the present application provides a financial auditing device, which includes at least the following units:

[0013] The collection unit is configured to obtain the target financial data to be processed from multiple financial data sources; the multiple financial data sources at least include: a financial data system in the target unit and a financial data system of an external cooperation unit;

[0014] The verification unit is configured to group the target financial data according to their respective corresponding data attributes to obtain multidimensional data groups; verify the multidimensional data groups from different dimensions through a data verification model to obtain verification results of the target financial data; the verification results at least include: rule verification results of each data item in the target financial data, abnormal data items in the target financial data that fail to pass the rule verification, and / or warning information corresponding to the abnormal data items; the data verification model is pre-configured with data verification strategies corresponding to different dimensions;

[0015] An evaluation unit is configured to perform compliance and risk analysis on the target financial data through a multidimensional analysis model to obtain analysis results of the target financial data; the analysis results include: at least one of a change trend between the target financial data, abnormal trend risk, financial indicator analysis, and abnormal fluctuation warning information;

[0016] The output unit is configured to generate an audit report corresponding to the target financial data based on the verification result and the analysis result; the audit report at least includes: the verification result of the target financial data, the compliance assessment, and the risk warning.

[0017] In a third aspect, an embodiment of the present application provides an electronic device, the electronic device comprising:

[0018] at least one processor, memory, and input-output unit;

[0019] The memory is used to store a computer program, and the processor is used to call the computer program stored in the memory to execute the financial audit method of the first aspect.

[0020] In a fourth aspect, a computer-readable storage medium is provided, which includes instructions, and when the instructions are executed on a computer, the computer executes the financial audit method of the first aspect.

[0021] The beneficial effect of the present application is that a financial audit method and related devices are provided. In the technical scheme, first, the target financial data to be processed is obtained from multiple financial data sources. Wherein, the multiple financial data sources at least include: the financial data system in the target unit and the financial data system of the external cooperation unit. Then, the target financial data is grouped according to the corresponding data attributes to obtain multidimensional data groups. Then, the multidimensional data groups are verified from different dimensions through the data verification model to obtain the verification results of the target financial data. Wherein, the verification results at least include: the rule verification results of each data item in the target financial data, the abnormal data items in the target financial data that have not passed the rule verification, and / or the warning information corresponding to the abnormal data items; the data verification model is pre-configured with data verification strategies corresponding to different dimensions. Then, the compliance and risk analysis of the target financial data is performed through the multidimensional analysis model to obtain the analysis results of the target financial data. The analysis results include: at least one of the change trend between the target financial data, the abnormal trend risk, the financial indicator analysis, and the abnormal fluctuation warning information. Finally, based on the verification results and the analysis results, an audit report corresponding to the target financial data is generated. The audit report shall at least include: verification results of the target financial data, compliance assessment, and risk warnings.

[0022] The technical solution of this application abandons the traditional model of relying on manual verification and simple rules, and introduces AI technology (i.e., data proofreading model), so that it can automatically identify and process data anomalies. With precise algorithms, the risk of errors caused by human negligence is greatly reduced, while significantly speeding up the verification process, simplifying the operation steps, reducing the difficulty of operation, and further improving the efficiency and reliability of data proofreading. With the help of multidimensional analysis models, we can deeply explore the relationship between financial data from multiple angles, perform multidimensional comparison and trend analysis, and achieve in-depth review of financial data to ensure the accuracy and compliance of the data. Through the automated generation process of audit reports, the cumbersome manual compilation process in related technologies is avoided, which not only reduces the burden on auditors, but also improves audit efficiency with high accuracy, which helps to adapt to the pursuit of intelligent and compliant processing of financial data by modern enterprises. In addition, the automated generation process of audit reports can also introduce independent interactions with audit units and auditors, making it more suitable for various financial audit scenarios, further accelerating the efficiency of financial audits, and improving the reliability of financial audits. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flowchart of a financial audit method according to an embodiment of the present application;

[0024] Figure 2 It is a schematic diagram of the principle of a financial audit method of an embodiment of the present application;

[0025] Figure 3 It is a schematic diagram of the principle of another financial audit method of an embodiment of the present application;

[0026] Figure 4 It is a schematic diagram of the principle of another financial audit method of an embodiment of the present application;

[0027] Figure 5 It is a structural schematic diagram of a financial auditing device according to an embodiment of the present application;

[0028] Figure 6 is a schematic diagram of the structure of an electronic device according to an embodiment of the present application;

[0029] Figure 7 It is a structural schematic diagram of a medium in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0031] Financial audit is an independent economic supervision activity, which aims to review and evaluate the authenticity, legality and effectiveness of the financial statements, financial revenue and expenditure and related economic activities of the audited unit. With the development of computer technology, more and more enterprises and institutions adopt financial data processing systems to improve the efficiency and accuracy of financial accounting audits and reduce audit costs.

[0032] However, in the related technologies, financial data processing systems are mostly focused on basic data recording, simple statistics, and manual verification functions. For example, for the auxiliary processing of a single process, such as bill auditing, contract auditing, and other single business processing links, other processes still require professionals to operate and complete audit reports based on their own experience. Therefore, the related technologies still have technical problems such as low automation in the audit process, low data processing efficiency, high difficulty in operation, easy to introduce errors due to improper manual operation, and difficult to ensure data quality. They cannot meet the demand for accelerated audit processes, nor can they adapt to flexible and changeable audit scenarios.

[0033] Specifically, the applicant found that modern financial information comes from a variety of sources, usually including data from different departments, subsidiaries, external partners, etc. However, the existing system has weak processing capabilities for heterogeneous data and lacks an automated fusion mechanism, which makes it difficult to unify and integrate data from different sources. For example, auditors need to manually merge data from multiple file formats, which not only increases the difficulty of operation, but also easily introduces errors due to improper manual operation, reducing the efficiency of data processing.

[0034] Secondly, the applicant found that the existing system usually relies on manual verification, and the manual verification process is prone to negligence, especially when facing a large amount of data, errors and omissions may occur in different links. Although some systems have simple rule verification logic, these simple rule verifications often stipulate a single function in a single scenario and cannot cover complex financial verification needs, such as cross-subject verification, historical trend analysis, and intelligent identification of abnormal points, resulting in the system's ability to ensure data quality is seriously insufficient.

[0035] Third, the applicant found that the audit of financial data not only requires the accuracy of the data, but also needs to meet various compliance requirements. However, in the existing system, financial personnel usually need to spend a lot of time and energy to analyze, organize and summarize the proofreading results in order to complete the compliance report. This process is time-consuming and laborious, and because it relies on manual compilation, there is a high error rate. At the same time, the reports generated by the existing system often only provide basic information, lack in-depth analysis, and cannot help management fully understand the risk points in the financial situation.

[0036] Finally, the applicant found that when the amount of data is large or the complexity of financial business increases, the existing system often shows significant performance bottlenecks and cannot meet the needs of real-time and efficient financial processing. Traditional processing methods based on manual proofreading or basic tools such as Excel spreadsheets are inefficient in big data scenarios and are prone to system crashes or data loss due to excessive operations.

[0037] It can be seen that the current financial processing system is difficult to support enterprises to conduct large-scale, multi-dimensional financial analysis and auditing, and cannot meet the auditing needs of modern enterprises. Therefore, it is urgent to propose a new technical solution to solve at least one of the above technical problems.

[0038] In order to solve at least one technical problem in the related art, the embodiment of the present application provides a financial audit method and a related device. In the technical scheme, first, the target financial data to be processed is obtained from multiple financial data sources. Among them, the multiple financial data sources at least include: the financial data system in the target unit and the financial data system of the external cooperation unit. Then, the target financial data is grouped according to the corresponding data attributes to obtain multidimensional data groups. Then, the multidimensional data groups are verified from different dimensions through the data verification model to obtain the verification results of the target financial data. Among them, the verification results at least include: the rule verification results of each data item in the target financial data, the abnormal data items in the target financial data that have not passed the rule verification, and / or the warning information corresponding to the abnormal data items; the data verification model is pre-configured with data verification strategies corresponding to different dimensions. Then, the compliance and risk analysis of the target financial data is performed through the multidimensional analysis model to obtain the analysis results of the target financial data. The analysis results include: at least one of the change trend between the target financial data, the abnormal trend risk, the financial indicator analysis, and the abnormal fluctuation alarm information. Finally, based on the verification results and the analysis results, an audit report corresponding to the target financial data is generated. The audit report at least includes: the verification results of the target financial data, compliance assessment, and risk warnings.

[0039] In summary, the embodiments of the present application can obtain target financial data from multiple financial data sources such as the target unit's own financial data system and the financial data system of external cooperative units, breaking the limitations of a single data source, making the data used as the basis for the audit more comprehensive and rich, covering a wider range of financial information, and helping to examine the financial status of the audited unit from multiple angles, avoiding the omission of important information due to a single data source, and improving the accuracy and reliability of the audit results.

[0040] Secondly, the target financial data is grouped according to their corresponding data attributes to form multi-dimensional data groups. This grouping method is in line with the multi-dimensional and multi-type characteristics of financial data, and can organize the messy data in an orderly manner, facilitating subsequent targeted verification and analysis from different dimensions, making data processing more logical and systematic, and laying the foundation for efficient and accurate audit work.

[0041] Furthermore, with the help of a data proofreading model that pre-configures data verification strategies corresponding to different dimensions, multidimensional data groups can be verified from multiple dimensions. Verification strategies in different dimensions can fully take into account the rule requirements of financial data in different aspects, such as data format, value range, logical relationship, etc., so as to accurately discover the rule verification status of each data item in the target financial data, accurately identify abnormal data items that have not passed the rule verification, and give corresponding warning information, which greatly improves the accuracy and comprehensiveness of data verification and reduces the omissions and errors that may occur in manual verification.

[0042] Then, the compliance and risk analysis of the target financial data is conducted using a multidimensional analysis model, which can deeply explore the internal connections between the data and reveal various contents such as the changing trends, abnormal trend risks, financial indicator analysis, and abnormal fluctuation warning information between the target financial data. This helps auditors not only stay at the surface of data verification, but also gain a deep insight into the operating conditions, potential risks, and other deep-seated issues reflected behind the financial data, providing strong support for comprehensive assessment of financial conditions.

[0043] Finally, an audit report is generated based on the verification and analysis results, covering key contents such as verification results of target financial data, compliance assessment, risk warning, etc. Such a report integrates various audit results and can provide complete, systematic and focused financial audit information to audit report users, so that they can understand the quality, compliance and risks of the audited unit's financial data at a glance, and make scientific and reasonable decisions accordingly.

[0044] The technical solution of this application abandons the traditional mode of relying on manual verification and simple rules, introduces a data proofreading model, can automatically identify and process data anomalies, and greatly reduces the risk of errors caused by human negligence with precise algorithms. At the same time, it significantly speeds up the verification process, simplifies the operation steps, reduces the difficulty of operation, and further improves the efficiency and reliability of data proofreading. With the help of multidimensional analysis models, we can deeply explore the association of financial data from multiple angles, perform multidimensional comparison and trend analysis, and achieve in-depth review of financial data to ensure the accuracy and compliance of data. Through the automated generation process of audit reports, the cumbersome manual compilation process in related technologies is avoided, which not only reduces the burden on auditors, but also improves audit efficiency with high accuracy, which helps to adapt to the pursuit of intelligent and compliant processing of financial data by modern enterprises. In summary, from data acquisition, processing, verification, analysis to final report generation, reasonable technical means and processes are used to effectively improve the quality and efficiency of financial audit work and enhance the value and reference of audit results.

[0045] The technical solution of the present application and the financial audit solution provided in the embodiments of the present application may also be executed by an electronic device, which may be a server, a server cluster, or a cloud server. The electronic device may also be a terminal device such as a mobile phone, a computer, a tablet computer, a wearable device, or a dedicated device (such as a dedicated terminal device with a financial audit method system). These electronic devices may also be equipped with the chips introduced in the above embodiments. Alternatively, these electronic devices may also be installed with a service program for executing the financial audit solution.

[0046] Figure 1 A flowchart of a financial audit method provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the method comprises the following steps:

[0047] 101, obtaining target financial data to be processed from multiple financial data sources;

[0048] 102, grouping the target financial data according to their corresponding data attributes to obtain multi-dimensional data groups;

[0049] 103, verifying the multi-dimensional data grouping from different dimensions through a data verification model to obtain a verification result of the target financial data;

[0050] 104. Perform compliance and risk analysis on the target financial data through a multidimensional analysis model to obtain analysis results of the target financial data;

[0051] 105. Generate an audit report corresponding to the target financial data based on the verification result and the analysis result.

[0052] In the embodiment of the present application, the multiple financial data sources at least include: a financial data system in the target unit and a financial data system of an external cooperation unit. Further, the financial data system in the target unit includes multiple financial subsystems. Each financial subsystem is associated with a department in the target unit, or each financial subsystem is associated with a branch of the target unit.

[0053] It is understandable that the financial data sources involved in the embodiments of the present application are diverse and hierarchical. On the one hand, it covers the financial data system of the target unit itself, which is further subdivided into multiple financial subsystems. These subsystems are closely linked to the organizational structure of the target unit. Some correspond to a department, which is responsible for collecting and organizing various financial revenue and expenditure, asset acquisition and depreciation data generated by the department during the operation process; some are associated with a branch, which gathers all-round financial information such as operation, cost, and income from the branch, and fully reflects the financial status of different sectors within the target unit. On the other hand, the financial data system of external cooperative units is also included. The external cooperative units may include suppliers and distributors in the upstream and downstream industrial chains. Their financial data systems provide key information such as fluctuations in raw material procurement prices and product sales collection cycles, enriching the financial data source from the perspective of industrial chain collaboration. By integrating these financial data sources of different levels and different subjects inside and outside the target unit, a solid data foundation is laid for subsequent in-depth and accurate financial audit work, making the audit basis more sufficient and comprehensive.

[0054] In an embodiment of the present application, the verification result at least includes: the rule verification result of each data item in the target financial data, the abnormal data item in the target financial data that fails the rule verification, and / or the warning information corresponding to the abnormal data item. Further optionally, the data proofreading model is pre-configured with data verification strategies corresponding to different dimensions.

[0055] In the embodiment of the present application, the verification result is a detailed feedback obtained after a comprehensive check of the target financial data. Specifically, the rule verification result of each data item is the result obtained after each data item in the target financial data is verified one by one according to the pre-set rules. It can clearly show whether each data item complies with the established rules, which is a basic judgment on data compliance. During the rule verification process, data items that do not comply with the preset rules will be identified and listed separately. These abnormal data items may imply problems such as data entry errors and abnormal business operations, and are the focus of audit attention.

[0056] For each abnormal data item that fails the rule verification, the system will give a corresponding warning message, detailing the cause of the abnormality, possible impact, etc., to provide clear guidance for auditors to further analyze and process. Further optionally, the data proofreading model is pre-configured with data verification strategies corresponding to different dimensions, which are the core basis for the verification work. In this application, the data proofreading model will set specific verification rules and methods for different types of data attributes and business scenarios based on multiple dimensions such as data format, numerical range, and logical relationship. For example, for amount data, a verification strategy for the numerical range will be set to specify a reasonable value range; for date data, a verification strategy for the data format will be formulated to ensure that it meets specific date format requirements.

[0057] In the embodiment of the present application, the analysis result includes: at least one of the change trend between the target financial data, abnormal trend risk, financial indicator analysis, and abnormal fluctuation alarm information. In the embodiment of the present application, the analysis result is the key information obtained after in-depth analysis of the target financial data. The analysis result can be a working paper (i.e., an audit record draft) that records the audit process, such as files such as monetary funds.xlsx. The analysis result can also be the source of audit report data.

[0058] Change trend: By analyzing the target financial data at different time nodes or business stages, the data's change trend, such as increase, decrease or stability, is displayed to help auditors understand the dynamic development of financial data, such as the change trend of income, cost and other data over time, so as to grasp the overall development trend of the company's finances.

[0059] Abnormal trend risk: Based on the analysis of the changing trend, identify abnormal trends that deviate from the normal development trajectory and assess the risks they may bring. For example, if a certain expense suddenly increases significantly and exceeds the reasonable range, it may indicate that the company has cost control problems or potential operating risks.

[0060] Financial indicator analysis: Calculate and analyze financial indicators from multiple aspects such as debt-to-asset ratio, gross profit margin, accounts receivable turnover rate, etc. These indicators are used to evaluate the financial health and operating performance of the company, providing a quantitative basis for a comprehensive understanding of the company's financial status.

[0061] Abnormal fluctuation warning information: When financial data fluctuates significantly and exceeds the normal fluctuation range, the system will issue an alarm message to prompt auditors to pay attention to possible problems. For example, if the sales data of a certain product suddenly plummets in a certain period of time, it may indicate changes in market demand, product quality problems, or impact from competitors.

[0062] In an embodiment of the present application, the audit report can be a standard report of the national recommended template, or it can be a non-standard report customized for the user. Further optionally, the audit report includes at least: the verification results of the target financial data, compliance assessment, and risk warnings. In an embodiment of the present application, the verification results of the target financial data: detailed listing of various situations of the previous rule verification of the target financial data, clearly indicating whether each data item complies with the established rules, and presenting abnormal data items that have not passed the verification one by one, and attaching corresponding warning information. This allows report users to intuitively understand the basis of the accuracy of the data, judge whether the data is reliable, and provide a basis for data quality for subsequent decision-making.

[0063] Compliance assessment: Based on the review of the target financial data and related business processes against laws, accounting standards, industry norms, etc., determine whether the target unit is compliant in terms of financial operations, data records, etc. For example, assess whether revenue recognition complies with accounting standards, whether expense listing complies with tax laws, etc. If there are non-compliance issues, the problem and potential impact will be explained in detail to help enterprises make timely rectifications and avoid regulatory risks.

[0064] Risk warning: Combine verification results with compliance assessment to dig deeper into potential risks. On the one hand, it points out the operational risks that may be caused by data anomalies, such as wrong decisions caused by data errors; on the other hand, it focuses on compliance risks, such as the risk of penalties for non-compliant operations. Through clear and explicit risk warnings, report users can be informed of hidden dangers in advance so that targeted preventive measures can be taken to ensure the financial security and sound operation of the enterprise.

[0065] For example, Figure 2 As shown, the following is an introduction to the above picture example in combination with steps 101 to 105.

[0066] Figure 2 It shows a complete process of financial data processing and report generation, which is closely related to steps 101 to 105. Each step has a specific task in the whole process, and together they realize the effective processing of financial data and the generation of audit reports.

[0067] Step 101 corresponds to ① data collection in the picture. First, Figure 2It shows asset data, liability data, equity data and profit and loss data, which are the main categories of financial data. In step 101, the target financial data to be processed is obtained from multiple financial data sources, as shown in the picture, and these different categories of financial data are collected to provide raw materials for subsequent processing. For example, various financial statements and account books are extracted from the company's financial system, covering specific data such as monetary funds, accounts receivable, short-term loans, accounts payable, paid-in capital, operating income, operating costs, etc., to ensure the comprehensiveness and integrity of the data, which is the basis of the entire process.

[0068] Step 102 corresponds to ② data preprocessing in the picture. Figure 2 Data preprocessing includes two aspects: data cleaning and format standardization. Data cleaning is to remove redundant, incomplete, and erroneous data, which is consistent with the concept of multi-level verification of financial data through the data proofreading model in step 102. For example, in the collected accounts receivable data, there may be duplicate records, missing customer information, or incorrect amounts. Through data cleaning, these problematic data can be eliminated or corrected. Format standardization is to unify the data format for calculation and processing, which helps to accurately analyze and calculate the data in subsequent steps, just like ensuring the consistency of the data format in step 102 for effective verification and analysis.

[0069] Step 103 corresponds to ③ proofreading and verification in the picture. Figure 2 The proofreading and verification phase in step 103 mentions predefined financial rules and financial audit proofreading, which corresponds to the compliance and risk analysis of the target financial data through the multidimensional analysis model in step 103. Predefined financial rules can be regarded as the basis for analysis and judgment in the multidimensional analysis model, for example, the reasonable range of gross profit margin and the normal range of current ratio are stipulated. In the actual proofreading and verification, the financial data is checked according to these rules, such as checking whether the valuation of each asset in the asset data complies with the accounting standards, and whether the recognition and measurement of debts in the liability data are accurate, etc., to ensure the accuracy and compliance of the data, which is an important step to discover potential problems and risks.

[0070] Step 104 corresponds to ④ analysis and calculation in the picture. Figure 2The analysis and calculation include indicator calculation and risk assessment, which is consistent with the requirement of further in-depth analysis of financial data in step 104. Indicator calculation is to calculate various financial indicators required in the financial audit result report, for example, gross profit margin is calculated based on the collected and processed operating income, operating cost and other data, and current ratio is calculated based on current assets and current liabilities data. These indicators are important bases for evaluating the financial status and operating results of the enterprise. Risk assessment is to indicate risks, calculate key financial indicators, and find out the financial risks that the enterprise may face, such as debt repayment risk, profit risk, etc., through the analysis of various financial indicators and comparison with historical data and industry data, so as to provide key information for subsequent report generation.

[0071] Step 105 corresponds to step ⑤ of generating a report in the picture, namely: Figure 2 The last step of the example process is to generate a report, which is exactly the same as the audit report corresponding to the target financial data generated based on the verification results and analysis results in step 105. The audit report includes at least the verification results of the target financial data, compliance assessment, risk warnings, etc., and the process in the picture provides rich data and analysis results to support the generation of such a comprehensive report through the previous steps of data collection, preprocessing, proofreading and verification, and analysis and calculation. For example, the report will present the accuracy of the data after proofreading and verification, the conclusion of whether the company's financial operations comply with laws and regulations based on the compliance assessment, and the potential risks and related suggestions found through risk assessment, etc., to provide valuable financial information and decision-making basis for corporate management, investors, regulators, etc.

[0072] In summary, Figure 2 It fully demonstrates the entire process from financial data collection to final report generation, which corresponds one-to-one with steps 101 to 105. Each step is indispensable and together constitutes a financial data processing and audit report generation system, ensuring the accuracy, reliability and usefulness of financial information.

[0073] The following describes the implementation method and technical effects of each step with reference to specific examples.

[0074] As an optional embodiment, in 101, the target financial data to be processed is obtained from multiple financial data sources. Specifically, in 101, first of all, the diversity of data sources is the key. These financial data sources at least include the financial data system in the target unit and the financial data system of the external cooperation unit. The target unit's own financial data system is composed of multiple financial subsystems, which are closely related to the internal organizational structure of the target unit. For example, each subsystem may correspond to a department, such as the sales department, the procurement department, etc., and will collect the financial data of the corresponding department such as income and expenditure, business transactions, etc.; or it may be associated with a branch office, and summarize the overall financial status information of the branch office. By integrating the data of these internal subsystems, the financial situation of different levels and different business segments within the target unit can be fully reflected.

[0075] The financial data system of external partners is also indispensable. External partners often involve various entities in the upstream and downstream of the industrial chain, such as suppliers and distributors. Their data systems can provide financial data such as fluctuations in raw material procurement prices, product sales collection cycles, and payment status of cooperative funds. From a more macro perspective of industrial collaboration, they provide key information for the audit and help analyze the financial status and potential risks of the target unit in the entire industrial chain.

[0076] In general, obtaining target financial data from multiple such financial data sources breaks the limitation of traditional reliance on a single data source, laying a solid data foundation for subsequent comprehensive, in-depth and accurate financial audit work, and enabling auditors to examine the financial activities of the target unit from a broader perspective, thereby obtaining more valuable and reliable audit results.

[0077] In practical applications, for example, the data collection module can be responsible for automatically collecting financial data from multiple sources, covering different financial subsystems, external cooperation units and other data sources. This module can receive data files in different formats and convert them into standardized internal data formats through interfaces. The data collected by this module covers important subject information such as receivables, payables, expenses, assets, etc. These subject information basically covers the core elements of the company's financial status, providing basic data support for a comprehensive understanding of the overall financial picture of the company.

[0078] The data collection module can use a variety of data receiving methods to receive data through API interfaces or file uploads. The API interface can achieve efficient docking between the system and different data sources, ensuring that data can be transmitted in real time and stably; while the file upload method provides convenience for the import of some offline data or batch data. Since the data file formats from different sources are often different, it may be CSV format, which records data in concise text form, or it may be Excel format, which has powerful table editing and data processing functions, etc., so the data collection module will further convert these data files in different formats into a standardized format, so that subsequent data processing links can be based on a unified and standardized data format to avoid compatibility issues caused by format differences and improve the efficiency and accuracy of data processing.

[0079] After collecting the data, in order to ensure the quality of the data, it will be preprocessed. This process mainly focuses on cleaning up various errors in the data, such as abnormal characters, such as some garbled characters, special symbols that do not meet the data specifications, etc., which may interfere with the subsequent data proofreading and calculation, affecting the accuracy of the final result. There is also the problem of null values. Null values ​​in the data will make the data incomplete, resulting in deviations in the analysis. And the situation of duplicate rows. Duplicate data will not only increase the amount of unnecessary data, but may also cause erroneous results in statistical analysis. By cleaning up these data errors, accurate and clean input data is provided for subsequent proofreading work and calculations of various financial indicators, ensuring that the entire financial data processing process can be carried out smoothly based on high-quality data, and laying a solid foundation for the final reliable financial analysis conclusions. In short, preprocess the collected data, clean up data errors such as abnormal characters, null values, and duplicate rows, and provide accurate input for subsequent proofreading and calculations.

[0080] As an optional embodiment, in 101, after obtaining the target financial data to be processed from multiple financial data sources, the target financial data may also be subjected to data cleaning processing; the data source of each data item in the cleaned target financial data is identified; for each data item, the format conversion branch model corresponding to the current data item is called based on the identified data source, and the current data item is subjected to format conversion processing through the called format conversion branch model to obtain the target financial data in a unified format.

[0081] In the embodiment of the present application, multiple format conversion branch models correspond one-to-one to multiple financial data sources respectively, and the conversion parameters in each format conversion branch model are obtained by adaptive learning based on data encoding features of the multiple financial data sources.

[0082] It is understandable that after obtaining the target financial data to be processed from multiple financial data sources, the first data cleaning process is of great significance. Due to the uneven data quality of different data sources, there may be various problems such as wrong values, duplicate records, missing values, and abnormal characters that do not meet the specifications. Data cleaning is to screen and correct these "impurities" to ensure the accuracy and completeness of the data and lay a good foundation for further data processing. For example, those obviously unreasonable values ​​caused by system failures or input errors are removed, or missing values ​​of key fields are filled. After this link, the quality of the target financial data is initially improved.

[0083] After completing data cleaning, the next step is to identify the data source for each data item in the cleaned target financial data. This step aims to clarify which financial data source each data item comes from, because different data sources often have their own unique data format characteristics. Correspondingly, multiple format conversion branch models are pre-set, and these format conversion branch models have a one-to-one correspondence with multiple financial data sources. When the data source of a data item is identified, the corresponding format conversion branch model can be accurately called based on this, preparing for subsequent targeted format conversion.

[0084] For each data item, the corresponding format conversion branch model is called to perform format conversion processing on it, with the ultimate goal of obtaining the target financial data in a unified format. It is worth mentioning here that the conversion parameters in each format conversion branch model are not set arbitrarily, but are obtained through adaptive learning based on the data encoding characteristics of multiple financial data sources. In other words, the model will automatically adjust and optimize the conversion parameters according to the differences in encoding methods, structural characteristics, etc. of data from different data sources to ensure that data items from different data sources can be accurately converted into a unified format that meets the requirements of subsequent processing. For example, some data sources may use a specific date encoding format, while others may use another format. The format conversion branch model can convert them all into a unified standard date format based on the learned encoding features, which is convenient for subsequent data integration and analysis operations.

[0085] Through the above steps, data obtained from different financial data sources and originally in different formats can be effectively integrated. This greatly improves the convenience of the subsequent data processing process, so that whether it is data proofreading, analysis, or generating audit reports, there is no need to spend a lot of energy to deal with the problem of inconsistent formats. Work can be carried out more efficiently and smoothly based on data in a unified format, ensuring the consistency and accuracy of the entire financial data processing process, and further improving the quality and efficiency of financial auditing and other related work. In short, through the above steps, data in different formats obtained from different financial data sources can be integrated to improve the convenience of subsequent data processing processes.

[0086] For example, in combination Figure 3 As shown, Figure 3 The classification of financial data and the corresponding data source examples are shown in the figure. These data sources are important sources for obtaining the target financial data to be processed. Figure 3 The asset data include "monetary funds.xlsx", "notes receivable.xlsx", "accounts receivable.xlsx", etc., all the way to "intangible assets.xlsx". These data files correspond to different categories of corporate assets. For example, "monetary funds.xlsx" records the company's cash, bank deposits and other monetary assets; "accounts receivable.xlsx" records in detail the amount of money that the company should collect from the purchasing unit or the receiving unit for selling goods or providing services. Through these asset data files, we can fully understand the composition, scale and distribution of corporate assets, which is an important data source for obtaining asset target financial data.

[0087] Liability data includes "short-term loans.xlsx", "notes payable.xlsx", "accounts payable.xlsx", etc., up to "taxes payable.xlsx". "Short-term loans.xlsx" records various loan information of the company's external borrowings with a term of less than one year (including one year); "accounts payable.xlsx" reflects the amount that the company should pay for business activities such as purchasing materials, goods and accepting labor supply. These liability data files provide a basis for obtaining the target financial data of the company's liabilities, which helps to analyze important financial indicators such as the company's debt burden and debt repayment ability.

[0088] Equity data includes "paid-in capital.xlsx", "capital reserve.xlsx", "surplus reserve.xlsx" and "undistributed profit.xlsx". "Paid-in capital.xlsx" reflects the total capital actually invested by the company's investors; "capital reserve.xlsx" records the part of the capital contribution received by the company from investors that exceeds its share in the registered capital or share capital; "surplus reserve.xlsx" and "undistributed profit.xlsx" respectively reflect the surplus reserve extracted from the net profit and the undistributed profit. These equity data files are the key to obtaining the target financial data related to the company's owner's equity, and are of great significance for evaluating the company's capital structure, shareholders' equity, etc.

[0089] Profit and loss data include "operating income.xlsx", "operating costs.xlsx", "sales expenses.xlsx", etc., until "other income.xlsx". "Operating income.xlsx" records the income obtained by the company in its daily business activities; "operating costs.xlsx" reflects the costs incurred by the company to obtain operating income; "sales expenses.xlsx" records the various expenses incurred by the company in the process of selling goods and materials and providing services. These profit and loss data files are important sources for obtaining target financial data related to the company's operating results. By analyzing these data, we can understand key financial information such as the company's profitability and cost control level.

[0090] Figure 3 The "① Data Collection" pointed by the arrow in the middle indicates that the above-mentioned various financial data files (such as the Excel files corresponding to asset, liability, equity and profit and loss data) are the objects of data collection. In actual operations, it is necessary to obtain the target financial data to be processed from these multiple financial data sources (that is, data files of different categories) and integrate them to provide a comprehensive and accurate data basis for subsequent financial analysis, auditing and other work. For example, when conducting a comprehensive analysis of the company's financial status, it is necessary to obtain information on the size and structure of the company's assets from the asset data, understand the company's debt situation from the liability data, grasp the owner's equity situation from the equity data, and analyze the company's profitability and cost and expense situation from the profit and loss data. All of these rely on effectively collecting target financial data from the multiple financial data sources shown in the figure. In summary, Figure 3 By displaying different types of financial data files in categories, examples of obtaining target financial data to be processed from multiple financial data sources are clearly presented. These data sources cover all important aspects of corporate finance and provide rich data support for the development of financial work.

[0091] In 102, the target financial data are grouped according to their corresponding data attributes to obtain multi-dimensional data groups.

[0092] As an optional embodiment, in 102, the subject identifier, time information, and data source information corresponding to each data item are extracted from the target financial data; according to the subject identifier corresponding to each data item, the target financial data are divided into subject groups corresponding to different subjects; according to the time information corresponding to each data item, the target financial data are divided into time series groups corresponding to different time periods; according to the data source information corresponding to each data item, the target financial data are divided into file source groups corresponding to different data sources.

[0093] In step 102, key information must first be extracted from the target financial data, namely, the account identification, time information, and data source information corresponding to each data item. The account identification can clearly identify the specific financial account category to which the data item belongs, such as accounts receivable, fixed assets, or management expenses, which is a classification basis for financial data based on accounting; the time information records the specific time point or time period when the data is generated or the related business occurs, which plays an important role in analyzing the changes and trends of financial data at different stages; and the data source information clearly indicates which specific financial data source the data item comes from, such as a module of the internal financial system of the enterprise, or a specific database of an external cooperative unit, which helps to trace the source of the data and understand its original characteristics.

[0094] In 102, after extracting the relevant information, the target financial data is divided into the subject groups corresponding to different subjects according to the subject identifier corresponding to each data item. Through this division method, the financial data of the same category of subjects can be gathered together, for example, all data related to the "operating income" subject are grouped into one group, and all data related to "accounts payable" are grouped into another group. In this way, when conducting financial analysis, audit verification and other work in the future, the data search, comparison and overall situation grasp for specific subjects will be more convenient and efficient, which will help to conduct special research and processing of each subject data from a financial professional perspective.

[0095] Next, in 102, according to the time information corresponding to each data item, the target financial data is divided into time series groups corresponding to different time periods. This can be divided according to the year, quarter, month, or even more detailed time dimensions such as day and week. For example, all financial data generated in the same month are grouped together, so that the overall financial situation of the enterprise in the time period can be clearly presented, and by comparing the data of different time series groups, the trend of financial data changes over time can be intuitively analyzed, such as observing how sales fluctuate in each quarter, the increase and decrease of costs in different years, etc., to provide a strong basis for grasping the dynamic development of corporate finance.

[0096] Finally, in 102, according to the data source information corresponding to each data item, the target financial data is divided into file source groups corresponding to different data sources. Since the data comes from multiple different financial data sources, the data characteristics, format specifications, etc. of each data source may be different. Through this grouping method, the data from the same data source can be integrated together, which is convenient for subsequent quality assessment, format unification, and analysis of the role of data from different sources in the entire financial system. For example, the data from the enterprise ERP system is grouped into one group, and the data from the external cooperative supplier database is grouped into another group, and their respective data situations and the correlation between them are studied separately.

[0097] Through the above grouping operations based on the three dimensions of subject identification, time information, and data source information, we finally obtained multidimensional data grouping. This multidimensional data grouping method fully considers the multi-faceted attribute characteristics of financial data, breaks the limitations of traditional single-dimensional grouping, and makes the organization and management of data more organized and systematic. It provides a multi-perspective, all-round infrastructure for subsequent data analysis, data proofreading, and comprehensive auditing, which can assist relevant personnel to examine and explore the value of financial data from different dimensions, more accurately identify problems, grasp trends, and assess risks, thereby effectively improving the quality and efficiency of financial-related work.

[0098] As an optional embodiment, in 103, a multi-level verification is performed on the financial data in different dimensions through a data verification model to obtain the verification result of the target financial data, including:

[0099] Through the basic audit layer of the data proofreading model, a pre-configured basic financial rule audit strategy is used to verify each data item in the target financial data to obtain a basic verification result; wherein, the basic financial rule audit strategy is used to proofread data items of at least one of the following dimensions: balance of debit and credit amounts, amount format, amount range, and account number format; through the financial audit layer of the data proofreading model, a cross-dimensional financial proofreading is performed on the target financial data to obtain a financial audit result, so as to realize automated proofreading and verification of the target financial data from different dimensions; through the output layer of the data proofreading model, the verification result is generated using the basic verification result and the financial audit result.

[0100] For example, assume that there is a target financial data of a manufacturing enterprise, which is now verified through the basic audit layer of the data verification model.

[0101] For example, when an enterprise records a sales transaction, accounts receivable increases by 10,000 yuan (debit) and main business income increases by 10,000 yuan (credit). According to the rules of the double-entry accounting method, there must be a debit for every credit, and the debit and credit must be equal. The basic audit layer will check whether the data items involved in this transaction are balanced in terms of debit and credit amounts based on the pre-configured basic financial rule audit strategy. If they are balanced, the basic verification result of this data in this dimension is in compliance with the rules; if the debit record is 10,000 yuan and the credit record is 9,000 yuan, it does not comply with the rules and will be marked as an abnormal data item, and a corresponding warning message will be generated to indicate that the debit and credit amounts are unbalanced.

[0102] For the various expense records of an enterprise, the amount filled in on the travel expense reimbursement form should be in a standardized digital format, such as "1234.56". If there is an amount written like "1,234.56" (some regions are accustomed to using commas as thousandths separators, but this does not meet the pure digital format required by the system) or "abc" (non-numeric characters), the basic audit layer can identify it. If it does not meet the amount format requirements, it will be judged as abnormal, reflected in the basic verification results, and a warning of the corresponding format error will be given.

[0103] Take the employee salary data of an enterprise as an example. Assuming that the salary of the same position in the industry of the enterprise is generally in the range of 5,000-15,000 yuan, and the amount range rules configured in the basic audit layer set reasonable upper and lower limits for salary. If a certain employee salary data item is recorded as 25,000 yuan, which exceeds the reasonable range, it will be judged as abnormal and marked in the basic verification results. At the same time, it warns that there may be errors in the salary data entry or special circumstances that need to be verified.

[0104] When recording financial accounts, each account has a corresponding number, such as "1001" for cash on hand, "1002" for bank deposits, etc., with fixed coding rules. If "1001a" appears in the financial records, which does not meet the account number format requirements, the basic audit layer can also detect it. The data items that do not meet the rules will be displayed as abnormal in the basic verification results, with a prompt that the account number format is incorrect.

[0105] After the basic audit layer uses these pre-configured basic financial rule audit strategies to comprehensively verify each data item in the target financial data, the basic verification result is obtained. This result records in detail whether each data item complies with the rules in these basic dimensions and any abnormal situations.

[0106] Taking the above-mentioned manufacturing enterprise as an example, the financial audit layer will conduct cross-dimensional financial proofreading.

[0107] For example, from the perspective of business process and account association, when an enterprise purchases raw materials, it will involve the linkage changes of multiple account data, such as the increase of the debit side of the "Raw Materials" account (indicating an increase in purchased inventory), the increase of the credit side of the "Accounts Payable" account (indicating an increase in the amount owed to suppliers), or the decrease of the credit side of the "Bank Deposits" account (indicating the use of bank deposits to pay for goods). The financial audit layer will verify whether the logical relationship between these different account data in this business scenario is correct and whether it complies with the actual procurement business process and financial accounting specifications of the enterprise.

[0108] From the perspective of time and financial indicators, when analyzing changes in a company's profitability, the operating income, cost, profit and other data of different time periods (such as the past few quarters) will be combined. Check whether the trend of these data changes in each time period is reasonable. For example, if the operating income continues to grow but the profit drops sharply, there may be cost control problems or other abnormal situations. The financial audit layer must analyze the correlation between various cost and expense data, income data, etc. from a cross-dimensional perspective to determine whether it conforms to normal financial logic and business rules.

[0109] After such cross-dimensional financial proofreading, the financial audit results are obtained. It automatically proofreads and verifies the rationality and accuracy of the target financial data from a more macro and multi-dimensional perspective, and discovers potential problems that are difficult to detect from a single dimension.

[0110] Finally, the output layer of the data proofreading model will generate the final verification results based on the basic verification results and financial audit results just obtained.

[0111] For example, the basic verification results show that the amount format of multiple expense reimbursements is incorrect, the amount of some business loans is unbalanced, and other abnormalities in the basic dimensions. The financial audit results also point out cross-dimensional problems such as the correlation between revenue and cost in the sales business of a certain quarter does not conform to the past operating trends. The output layer will integrate this information to generate a complete verification result, clearly listing all abnormal data items found in the target financial data, and specifically explaining what problems each abnormal item has in the basic dimension and cross-dimensional proofreading, and attaching corresponding detailed warning information, such as "The amount format of expense reimbursement form number 001 is incorrect, please verify and re-enter; the revenue and cost change trend of sales business number 005 in this quarter does not conform to normal business logic, and it is recommended to further review the relevant business processes and accounting methods" and so on.

[0112] The verification results generated in this way comprehensively and in detail reflect the overall situation of the target financial data, providing an important basis for subsequent further analysis, processing and final audit report generation.

[0113] In summary, through the verification operations at different levels of the data proofreading model in this optional embodiment and the corresponding result generation process, the target financial data can be checked in depth and comprehensively to ensure the quality and compliance of the financial data.

[0114] Optionally, the basic audit layer of the data verification model is an important part of the preliminary and basic verification of the target financial data. It mainly verifies each data item from the following dimensions through the pre-configured basic financial rule audit strategy:

[0115] Account level verification: At the account level, strictly follow the basic financial principle of "debit total = credit total". For each account, such as asset accounts, profit and loss accounts, check whether the total debit amount is equal to the total credit amount to ensure that the debit and credit amounts of each account are balanced.

[0116] Balance sheet verification: For the balance sheet, verification is performed based on the formula "beginning balance + current period debit amount - current period credit amount = ending balance". By calculating and comparing the beginning, current period debit and ending balances of each account, errors in balance calculation can be found in a timely manner. If the calculation result is inconsistent with the actual recorded ending balance, the data item will be marked as abnormal, indicating that there may be problems such as data entry errors or improper accounting processing.

[0117] Amount rationality check: Ensure that all amounts are positive and within a reasonable business range. Set corresponding amount range standards according to the nature of different accounts and business logic. For example, for daily expense accounts, the amount will not be negative under normal circumstances; for large purchase accounts, the amount cannot exceed the company's pre-set budget limit. In this way, abnormal amount data that is obviously inconsistent with business reality can be quickly screened out.

[0118] Amount format standard check: strictly check the format of the amount field, clean up the non-numeric characters or symbols contained in it, such as converting "$10,000" to the standard "10000" format, remove extra spaces or special characters in the amount field, ensure the standardization and consistency of the amount data, and facilitate subsequent data processing and analysis. Once data that does not meet the format requirements is found, it will be marked and processed in a timely manner.

[0119] It is understandable that in addition to the regular amount range and format checks, attention should also be paid to whether the account amount is abnormally high. For example, the daily office expense account usually remains at a level of several thousand yuan. If the data for a certain month is suddenly recorded as 500,000 yuan, which is far beyond the normal range, this situation needs to be checked immediately. It may be a data entry error or there may be special business circumstances that require further verification.

[0120] In actual applications, check whether the length of the account number meets the standards defined by the company or industry. For example, if the length of the account number is fixed at 5 or 8 digits, then for numbers that do not meet this length requirement, further verification and processing are required to mark them as data with possible problems.

[0121] Check the account number according to the predefined grouping rules. For example, account numbers starting with "1" belong to the asset category, and those starting with "6" belong to the profit and loss category. By judging the first digit of the number, confirm whether the category of the account number is correct to prevent the number from not matching the account category.

[0122] Not only should the accuracy of the first-level account number be checked, but also the logical consistency check of the second-level account number under it should be performed. For example, the second-level account under the first-level account "1001" should comply with the numbering rules, such as "100101", "100102", etc. If there is a situation that does not conform to the numbering logic, such as "1001A1", etc., it is necessary to confirm whether it is an exception under a specific project, otherwise it will be marked as an exception to ensure the integrity and accuracy of the account numbering system.

[0123] For example, refer to Figure 4 As shown in Figure 1, the data verification model is a system framework for ensuring the accuracy and consistency of financial data. Figure 4 The process in Figure 1 is a specific application example of the model in the proofreading and verification stage, which mainly focuses on the proofreading of account numbers and debit and credit amounts. Through a series of steps, it ensures the accuracy of financial data in these two key aspects, thus providing important support and guarantee for the entire data proofreading model.

[0124] Account number & loan amount: This is the core content of the proofreading and verification, which clarifies that the proofreading work is mainly aimed at the account number in the financial data and the corresponding loan amount. The account number is an important identifier for the classification and management of financial data, and the loan amount is a key value reflecting financial transactions. The accuracy of the two directly affects the quality and reliability of financial data.

[0125] Unified numbering format (truncate the first 4 digits for standardized representation): In order to facilitate subsequent proofreading and processing, the account numbers are first standardized in a unified format. The specific approach is to truncate the first 4 digits of the account number for standardized representation, which can eliminate the confusion and errors caused by inconsistent account number formats and ensure that in the subsequent proofreading process, comparisons and verifications can be made based on unified standards. For example, there may originally be account numbers in a variety of different formats, such as "100101" and "01001". After the first 4 digits are uniformly truncated, they are all standardized to "1001" and other forms, which improves the consistency and comparability of the data.

[0126] Figure 4In the process of unifying the numbering format, the chronological account is operated. The chronological account is a book that records the economic transactions one by one in the order of time. It records the details of each financial transaction. In this step, the total amount of credit and debit is calculated. By adding up the debit and credit amounts of each transaction in the chronological account, the total credit and debit amounts are obtained, which provides basic data for the subsequent verification with the data in the balance sheet.

[0127] Figure 4 In the balance table, the number, current period debit and current period credit columns are directly returned. The balance table is a report that reflects the balance of each accounting account on a specific date. It is related to the chronological account but different. Here, the number and the data of the current period debit and credit columns in the balance table are directly obtained to check with the total amount of debits and credits calculated by the chronological account.

[0128] Figure 4 In the proofreading process, checking the debit and credit amounts is a key step in the entire proofreading process. Specific formulas are used to check whether the debit and credit amounts are balanced. The specific formulas are "debit check = current period debit - total debit" and "credit check = current period credit - total credit". The "current period debit" and "current period credit" here are data obtained from the balance sheet, while the "total debit" and "total credit" are the sum of the debit and credit amounts calculated from the chronological account. By calculating these two differences, if the result is 0, it means that the debit and credit amounts are balanced and the data is accurate in this respect; if the result is not 0, it indicates that there is an imbalance between debit and credit, which requires further inspection and verification.

[0129] Figure 4 In the data verification model, the verification results are output, and finally the verification results are output, whether the debits and credits are balanced or unbalanced, they are presented in a clear way. This result is one of the important outputs of the data verification model, which provides key information about the accuracy of account numbers and debit amounts for subsequent data analysis, auditing, and financial decision-making. If an imbalance is found, relevant personnel can quickly locate the problem based on the output results, conduct targeted investigations and corrections, and ensure the quality and reliability of financial data.

[0130] In summary, Figure 4 The example in the article shows in detail the specific proofreading process of the data proofreading model for account numbers and debit and credit amounts in the proofreading and verification phase. By unifying the numbering format, processing the chronological account and balance sheet separately, verifying the debit and credit amounts, and outputting the verification results, it achieves accurate proofreading of these two key financial data elements, providing a strong guarantee for the accuracy and reliability of the entire financial data. It is an important part of the data proofreading model in practical applications.

[0131] As an optional embodiment, in 103, a cross-dimensional financial proofreading is performed on the target financial data through the financial audit layer of the data proofreading model to obtain a financial audit result, including:

[0132] Through the file verification module of the financial audit layer, the target financial data is subjected to cross-file verification according to the file source grouping and the subject grouping to obtain the first financial audit result; through the subject verification module of the financial audit layer, the target financial data is subjected to cross-subject verification according to the subject grouping based on the correlation between each subject to obtain the second financial audit result; through the multi-dimensional verification module of the financial audit layer, the target financial data is subjected to time series trend verification and multi-dimensional correlation analysis according to the corresponding subject grouping, time series grouping and file source grouping to obtain the third financial audit result.

[0133] For example, suppose a group company has multiple subsidiaries, and the financial data of each subsidiary is stored in different file sources, and all of them cover subjects such as "accounts receivable" and "fixed assets". The file verification module will extract data from different subsidiaries (i.e., different file source groups) but belonging to the "accounts receivable" subject for cross-file verification. For example, the accounts receivable file record of subsidiary A shows that the accounts receivable balance with customer X at the beginning of this quarter is 500,000 yuan, while the relevant files of subsidiary B show that the accounts receivable balance of the same customer X under the same event during the same period is 450,000 yuan. This inconsistency will be identified by the file verification module. In this way, through this cross-file verification, the data differences caused by different file sources but involving the same business subjects can be accurately found, avoiding information deviations caused by data islands, ensuring the data consistency of key financial subjects at the group level, and providing a reliable data foundation for subsequent consolidated statements and overall financial analysis.

[0134] For example, in the daily operation of an enterprise, there is a close correlation between the "main business cost" and "inventory goods" accounts. When an enterprise sells products, the inventory goods decrease, while the main business cost increases, and the two follow a certain matching principle in terms of amount. Based on this correlation, the account verification module will group the target financial data by account and perform cross-account verification on the two accounts. For example, a company sold a batch of products this month, and the inventory goods account showed a reduction of goods worth 800,000 yuan, but the cost increase recorded in the main business cost account was only 500,000 yuan, which obviously did not conform to the flow logic of cost and inventory, and the module would mark this anomaly. Thus, it helps to discover the logical matching problems of financial data between accounts, allowing enterprises to promptly detect possible cost accounting errors, inaccurate inventory valuation or business process loopholes, ensure the accuracy of financial data in the account system, and assist management in making accurate business decisions, such as optimizing cost control strategies and adjusting inventory management methods.

[0135] Taking an e-commerce company as an example, the multi-dimensional verification module will group the target financial data by subject (such as revenue, marketing expenses, traffic acquisition costs, etc.), time series grouping (divided by quarters in the past year), and file source grouping (different data sources such as online platform sales data and offline promotion data), and perform time series trend verification and multi-dimensional correlation analysis. For example, from the time series trend, the marketing expenses of the e-commerce company have continued to rise in the past four quarters, but the traffic growth corresponding to the traffic acquisition cost is not obvious, and even declines in some quarters; from the multi-dimensional correlation analysis, the online platform sales data shows that the revenue of a certain type of popular goods has increased significantly during the promotion season, but the related traffic acquisition costs have not increased synchronously. These abnormal trends and correlation mismatches will be discovered. In this way, the deep-seated problems hidden in the financial data can be fully explored, and a three-dimensional portrait of the company's financial status can be presented from the complex perspectives of time dynamic development and multi-data sources and multi-subject cross-correlations. It not only helps companies to capture business risks in a timely manner, such as ineffective marketing investment and business growth bottlenecks, but also helps to optimize resource allocation, adjust the input-output strategy of each business link based on accurate data analysis, and improve overall operational efficiency.

[0136] In summary, through the coordinated operation of these three modules, the financial audit layer has achieved comprehensive and in-depth cross-dimensional proofreading of the target financial data, greatly improved the quality of financial data, and provided solid support for corporate financial management, decision-making, etc.

[0137] In the file verification module of the financial audit layer, cross-file verification plays a key role, aiming to ensure the consistency and accuracy of financial data from different file sources on key subjects. When faced with independent financial files of multiple subsidiaries under the group company, such as chronological accounts and balance sheets, the first thing to do is to accurately read the relevant data from these different files, focusing on the account number and the corresponding debit and credit amount fields. However, due to the differences in file formats and recording habits of each subsidiary, preprocessing is necessary for subsequent smooth comparison. For example, standardize the names of different fields, unify different expressions such as "debit total" and "current debit", and convert the amount fields into numeric types, remove possible interference characters such as "￥" and ",", and properly handle missing values, replacing them with blanks or specific placeholders to ensure that the data format is completely consistent and prepare for the next step of integration.

[0138] The account number can be used to merge scattered data from different files into a unified data table. For example, for a specific account number, the total debits and total credits are extracted from the chronological account, and the current period debits and current period credits are captured from the balance sheet. These four data are accurately placed in the same row, and a close correspondence is established through the account number, so that the data on the same account from different file sources can be summarized for easy centralized comparison.

[0139] After completing the data merger, the difference between the two source data is calculated. For each row of merged data, carefully check the debit and credit amounts. If the difference is not zero, it means that different file sources have different records for the same item of the same subject. At this time, immediately record the subject number, difference amount and related file source information in detail. These records will serve as key clues for subsequent in-depth analysis to help auditors quickly locate the source of the problem.

[0140] Faced with numerous complex table data, the first step is also to conduct comprehensive cleaning and standardization. Remove garbled characters, irregular date formats and other abnormal characters, unify the data format, such as unifying all dates into the "YYYY-MM-DD" format, and unifying the amounts into two decimal places, ensuring that the field names and data types of each table are completely consistent, laying the foundation for accurate comparison.

[0141] With the help of account number or other key fields with unique identifiers, the data of different tables are compared line by line. The focus is on checking whether the debit and credit amounts in the matched records are consistent. Once it is found that the amount corresponding to a certain account number exists in one table but not in another table, or the amount values ​​of the same number in different tables are very different, which obviously does not conform to normal financial logic, it will be ruthlessly marked as a difference record and a detailed difference report will be generated, which clearly lists the different account numbers, the tables involved, and the specific amount differences.

[0142] The identification of redundant records mainly focuses on two typical situations. On the one hand, there are redundant records, that is, some account numbers only appear in one table and are not found in other related tables. Such isolated records are likely to be redundant information. On the other hand, there are duplicate records, which are manifested as some account numbers appearing multiple times in the same table, but the total amount is quite different from the corresponding amount in another table. For example, a certain account number has two records in the chronological account, with amounts of 100 and 200 respectively, while there is only one record in the balance sheet, with an amount of 250. The difference between the two amounts is quite large. At this time, it is decisively marked as redundant and different, and included in the scope of key investigation.

[0143] After the above series of rigorous and complex cross-file verification processes, a valuable analysis report will be output in the end. This report is an intermediate output result. It clearly lists the specific differences and redundancies, down to each account number, the corresponding loan amount, and the respective source table. For auditors, this report can quickly locate the problem, whether it is data entry errors, asynchronous file updates, or other potential data risks. Through such a systematic cross-file verification method, the efficiency and accuracy of data verification are effectively improved, providing a data basis for subsequent analysis and decision-making of the entire financial audit layer, ensuring the high-quality integration and application of financial data at the enterprise group level.

[0144] To sum up, cross-file verification, as an important means of the file verification module and even the entire financial audit layer, effectively ensures the reliability and consistency of multi-source financial data through rigorous process design and meticulous operation execution, and helps corporate financial management move to a higher level.

[0145] Optionally, when reading the chronological accounts, balance sheets and other financial documents of different subsidiaries, standardize the various field names such as "debit total" and "current period debit" to eliminate the risk of confusion caused by differences in expression. At the same time, strictly convert the amount field to a numeric type, carefully remove characters such as "￥" and "," that may interfere with the calculation, and use scientific methods to handle missing values, replacing them with blanks or suitable placeholders, to ensure that the data format involved in the verification is accurate from the beginning, laying a solid foundation for subsequent accurate comparison.

[0146] Using the account number as an index, the debit and credit data of the same account can be accurately captured from different file sources, such as extracting the total debit and credit from the chronological account, extracting the current period debit and credit from the balance sheet, and integrating these four key data accurately in the same row. This close association construction enables the debit and credit data of the same account and the same matter in different files to be accurately summarized and classified, avoiding data mismatch and allowing balance verification to be carried out on the correct data combination.

[0147] After completing the data merger, check the debit and credit amounts for each row of integrated data. Once the calculated difference value is not zero, immediately record the account number, the exact difference amount, and the corresponding file source information in detail. Such a delicate operation ensures that any imbalance in the amount of credit and debit can be captured sensitively, and the source of the problem can be quickly traced back based on the key clues recorded, and whether it is caused by data entry errors or poor file update connection, etc., to ensure the accuracy of the verification in all aspects.

[0148] Through the above process, the balance check of loan amounts in cross-file verification can ensure accuracy to the greatest extent, laying a solid foundation for the reliable integration and application of multi-source financial data of the enterprise.

[0149] As an optional embodiment, in 102, the target financial data is cross-file verified according to the file source grouping and the subject grouping by the file verification module of the financial audit layer to obtain a first financial audit result, including:

[0150] Compare loan data items in the target financial data that come from different file source groups but belong to the same subject group to identify and mark the difference data items in the loan data items; identify and mark redundant data items corresponding to the same financial data in different file source groups according to the subject grouping, time series grouping, and file source grouping corresponding to each of the target financial data; generate a first financial audit result containing difference data items and / or redundant data items, and construct a corresponding cross-file analysis report based on the first financial audit result.

[0151] For example, suppose a large chain enterprise has many stores. Each store is an independent file source group, and their respective financial data will be aggregated to the headquarters for audit analysis. The financial data covers multiple subject groups such as "inventory goods", "sales revenue", and "employee salaries".

[0152] Using the file inspection module of the financial audit layer to monitor, it can be found that under the "Inventory" account group, the financial file records of store A show that a batch of goods were purchased and put into storage at the beginning of this month, and the debit record increased the inventory amount by 50,000 yuan; while the relevant files of store B at the same time, for the same batch of goods, the debit amount was 48,000 yuan. The file inspection module can quickly find the difference of 2,000 yuan by comparing the debit and credit data items from different stores (i.e. different file source groups) but belonging to the same account group of "Inventory", mark it as a difference data item, and record the stores and specific business matters involved in detail.

[0153] Using the file inspection module at the financial audit level to monitor, it can be further discovered that for the "sales revenue" account, during the promotional activity of store C, the credit record of the sales revenue for the day was 12,000 yuan, while the credit record of the sales revenue of store D during the same period and activity was only 8,000 yuan. After comparison, the difference data item of 4,000 yuan was accurately identified and marked, providing clues for subsequent in-depth investigation of the reasons for the difference in sales data, such as differences in the implementation of promotion strategies, different accounting time nodes, or errors in data entry.

[0154] From the perspective of time series grouping, data is divided by quarter. In the financial data of the first quarter, under the "employee salary" account of store E, there is an employee bonus payment record that appears twice in the January file, and the amount is the same. This is a duplication and redundancy within the same file source group. When comparing the data of the same account of store F in the same quarter, it is found that the extra record of store E does not appear in store F, that is, the same financial data corresponding to different file source groups is redundant. The file verification module will identify and mark such redundant data items, and analyze whether there are problems such as repeated calculation of salaries or data transmission errors.

[0155] For example, when checking the annual data of the "Inventory Merchandise" account, the inventory adjustment record of a certain type of merchandise in store G during the year is only reflected in its own file source grouping, and there is no trace of it in the inventory merchandise data of the same category in other stores during the same period. This may be an isolated and redundant record, which will also be marked as redundant. Check whether it is a special treatment by the store alone that has not been reported, or whether it was entered by mistake.

[0156] Through this cross-file verification method, the differences and redundant data items hidden in the massive and scattered financial data can be accurately found. Whether it is data inconsistency caused by geographical differences in stores, different business operation habits, or redundant information caused by system transmission and human input errors, they are all exposed, providing auditors with clear problem directions, greatly saving time and energy for troubleshooting. Ensure that the same subject grouping financial data reported by different stores (file source groups) can achieve logical unity at the headquarters level and eliminate data conflicts. Make financial analysis and consolidated report preparation based on these data have a reliable data foundation, such as accurately calculating the overall inventory cost of the enterprise, truly reflecting the scale of sales revenue, etc., to improve the accuracy of financial management decisions. Continuously mark and process differences and redundant data items, so that stores pay more attention to data quality in the subsequent data reporting process, standardize business operation procedures and data recording methods. At the same time, based on the problems reported in the cross-file analysis report, the headquarters can optimize the data collection and integration system in a targeted manner, forming a virtuous cycle of data quality improvement, and escorting the overall financial operation of the enterprise.

[0157] In summary, the file verification module at the financial audit layer effectively improves the quality and availability of target financial data through a rigorous cross-file verification process, providing strong support for the company's complex multi-source financial data management.

[0158] As an optional embodiment, in 103, the subject verification layer of the data verification model performs cross-subject verification on the target financial data according to the subject grouping according to the correlation between the subjects, and obtains a second intermediate verification result, including:

[0159] For each data item to be audited, determine the associated subject grouping associated with the subject grouping in which the current data item is located; perform linkage verification on the subject grouping in which the current data item is located and the associated subject grouping to obtain the association relationship between the current data item and the relevant data items in the associated subject grouping; and generate the second intermediate verification result based on the association relationship.

[0160] For example, suppose there is a manufacturing company whose financial data covers multiple account groups such as "raw materials", "production costs", "inventory", "main business income", and "main business costs".

[0161] When focusing on the data items of the "production cost" account group, the closely related account groups include "raw materials", "inventory goods" and "employee wages payable". Because in the production process, raw materials are the key elements that constitute the product entity, and their input directly affects the production cost, so "raw materials" is a related account; after the production is completed, the product is put into storage, and the "inventory goods" account corresponds to it, reflecting the change in inventory status; and the labor input of workers generates salary expenses, which are calculated through "employee wages payable" and included in the production cost.

[0162] Similarly, for the "main business income" account grouping, the related account groups include "accounts receivable" and "bank deposits". When an enterprise sells products and realizes income, if the money is not collected immediately, it will form accounts receivable. If the money is collected immediately, it will be reflected in the bank deposit account. They are closely linked to the confirmation of main business income, reflecting the connection between capital flow and income realization.

[0163] Taking "production cost" as an example, during the production of a batch of products, the debit side of the "raw materials" account records the amount of raw materials invested as 80,000 yuan, and the debit side of the "production cost" account records the cost increase corresponding to the raw materials used for production of this batch of products, which should be about 80,000 yuan (considering reasonable losses and other factors). If the data of the two are very different, such as the amount of production cost records only 60,000 yuan, it means that there may be problems such as inaccurate accounting of raw materials and unreasonable cost allocation, and this abnormal relationship is captured. Looking at "main business income" and "accounts receivable", when a certain sales business occurs, the main business income credit records the income amount of 50,000 yuan. If the debit side of accounts receivable does not increase accordingly during the same period (excluding the situation of receipt), or the increase amount does not match it, such as accounts receivable only increase by 30,000 yuan and there is no other reasonable explanation, it means that there may be risks such as the synchronization of income recognition and payment records, omission of accounts receivable or false increase of income, and the linkage verification reveals this abnormal association.

[0164] Based on the abnormal relationship found above, the generated second intermediate verification results will be listed in detail, such as "In [specific product batch production business], the association between 'raw materials' and 'production cost' accounts is abnormal, the raw materials investment is 80,000 yuan, and the production cost record is only 60,000 yuan. It is recommended to verify the cost accounting method"; "Under [a certain sales business number], 'main business income' is 50,000 yuan, and 'accounts receivable' only increased by 30,000 yuan during the same period. The income and payment records are questionable, and the business process and accounting basis need to be further reviewed." These results provide accurate clues for subsequent in-depth audits.

[0165] In this way, by conducting cross-subject verification based on the correlation between subjects, the limitations of isolated auditing of a single subject can be broken and the internal logical chain of financial data can be deeply explored. The subject data involved in a series of business links, such as raw material procurement, production and processing, product sales, and capital recovery, can be comprehensively examined for their rationality and accuracy, and the financial context of the company's business activities can be accurately grasped. Many problems caused by the imbalance of subject correlation relationships can be effectively discovered, such as cost accounting errors, false revenue recognition, asset valuation deviations, etc. Whether it is due to human negligence, business process loopholes or risks caused by illegal operations, they can be fully exposed under this cross-subject verification, providing early warning for enterprises and preventing financial crises. Based on the precise correlation analysis, detailed intermediate verification results are generated, so that auditors do not need to blindly check in massive data like looking for a needle in a haystack, but directly based on the problem points in the verification results, they can conduct targeted and in-depth verification of related businesses and accounts, greatly saving audit time, improving audit accuracy, and ensuring efficient and high-quality completion of financial audit work.

[0166] To sum up, the subject verification layer of the data proofreading model uses cross-subject verification methods to inject strong support into the audit of corporate financial data and ensure the reliability and robustness of corporate financial information.

[0167] It is understandable that in actual applications, at the subject verification layer of the data proofreading model, cross-subject verification relies on rigorous verification logic to deeply explore the relationship between financial data and ensure data quality.

[0168] When verifying the balance sheet, the first task is to ensure the balance of the basic financial equation "Total Assets = Total Liabilities + Total Owners' Equity". The account verification layer is like a rigorous accountant, accurately extracting the three key fields of "Total Assets", "Total Liabilities" and "Total Owners' Equity" from the huge data table of the balance sheet. For "Total Assets", read its value directly, while for "Total Liabilities" and "Total Owners' Equity", add the values ​​of the sub-classification accounts they each contain to calculate the theoretical total value. For example, "Total Liabilities" covers sub-classifications such as "Long-term Liabilities" and "Current Liabilities". The values ​​of these sub-classification accounts must be accurately summed to ensure that they are strictly consistent with the value of the main account of "Total Liabilities", laying the foundation for subsequent accurate verification.

[0169] After the theoretical value is calculated, it is then compared with the value of the "Total Assets" field directly read to calculate the difference between the two. If the difference is not zero, it means that there is a problem with the balance sheet. This situation is immediately marked as abnormal and the relevant account names are recorded in detail, such as the specific sub-classification account involved in the imbalance, as well as the precise field name and difference value, to provide key clues for the auditors to conduct in-depth investigations.

[0170] In the verification of the income statement, the focus is on the relationship between the summary of entries of each account and the total income or total cost. The account verification layer will first extract income accounts and cost accounts from the income statement. For income, add the amounts of each classified account such as "main business income" and "other business income" to obtain the total income of the classified account, and then compare it with the "total income" field one by one. Similarly, for cost accounts, summarize "main business cost", "management expenses", "financial expenses" and so on according to the rules, calculate the total cost of the classified account, and then compare it with the "total cost" field. Once an inconsistency is found, it means that an abnormal environment in the financial data chain has been discovered. In this case, it is necessary to further check the amount details of each classified account to check whether there are omissions or duplicate records of amounts. For example, if it is found that there are multiple duplicate entries in "main business income", it is necessary to use data cleaning rules to merge them and re-summarize them to ensure the accuracy and completeness of the income data. Faced with accounts with more complex relationships, the account verification layer demonstrates a strong cross-table operation capability. Taking the "Total Assets" of the balance sheet as an example, it is not limited to the verification within its own table, but also needs to compare data with other tables, such as related accounts in the account balance sheet, to ensure the consistency of asset data from different table perspectives.

[0171] At the same time, there is an inherent logical connection between the "net profit" in the income statement and the changes in the "owner's equity" in the balance sheet. The verification process needs to verify whether the "net profit" is correctly reflected in the changes in the "owner's equity", that is, to check whether the increase or decrease in the "owner's equity" matches the "net profit" and other factors that may affect the equity (such as dividends, capital increase, etc.). Through this cross-table linkage verification, it is like weaving a tight net for financial data, catching potential logical errors and omissions, and ensuring the integrity and accuracy of the report data in all aspects.

[0172] To sum up, the cross-subject verification at the subject verification layer effectively improves the quality of financial data through sophisticated operations between the balance sheet, income statement and across tables, relying on rigorous verification logic, providing a solid and reliable guarantee for financial audits, making it impossible for problems hidden behind the data to hide, and helping to grasp the financial health status more accurately.

[0173] As an optional embodiment, in 103, the target financial data is grouped according to the corresponding subjects, time series groups, and file source groups through the multi-dimensional verification layer of the data verification model, and time series trend verification and multi-dimensional correlation analysis are performed to obtain a third intermediate verification result, including:

[0174] The target financial data is predicted for its changing trend according to their respective corresponding time series groups, and the actual values ​​of each data item in each time series group are compared with the predicted changing trend values ​​for consistency; volatility analysis is performed on data items that are inconsistent between the actual values ​​and the predicted changing trend values ​​to identify whether the inconsistent data items are abnormal jumps; if they are abnormal jumps, the identified inconsistent data items are marked as abnormal data items, and the corresponding volatility analysis results are marked.

[0175] For example, suppose an e-commerce company has financial data covering account groups such as "sales revenue", "marketing expenses", and "traffic acquisition costs". The data comes from different file source groups such as online platform sales data and offline promotion data, and the time series groups are divided by quarter. For the "sales revenue" account, based on the historical data of the past few years, the multi-dimensional verification layer uses the time series analysis algorithm to predict the sales revenue change trend values ​​of different months in the next quarter. For example, based on the previous data rules, it is predicted that the sales revenue in January should increase by 10% month-on-month to 5 million yuan. However, the actual value shows that it is only 4 million yuan that month, and it is found that the actual value is inconsistent with the predicted change trend value. Similarly, in the "marketing expenses" account, if it is predicted that the marketing expenses of this quarter should increase steadily with the expansion of business, with an average monthly increase of 8%, however, the actual data is found that the marketing expenses in a certain month suddenly soared by 50%, far exceeding the expected trend, which also triggered the inconsistency alarm. For the 4 million yuan data in January in which the actual value of "sales revenue" is inconsistent with the predicted value, the multi-dimensional verification layer further conducts volatility analysis. It not only compares the revenue fluctuations of adjacent months, but also refers to the average fluctuation range of the same industry during the same period. After analysis, it was found that the sales revenue of similar e-commerce companies in the surrounding area generally increased that month, and the historical fluctuations in the same period had never been so severe. Comprehensively judging that this was an abnormal jump. Regarding the month when "marketing expenses" soared abnormally, a deep investigation of the expense details revealed that it was a huge amount of ineffective advertising that caused the cost to soar, and the effect of this advertising channel in the past was extremely poor. This was undoubtedly an abnormal jump that deviated from the normal marketing investment rhythm, rather than a reasonable fluctuation.

[0176] In the above steps, the hidden anomalies in the financial data can be detected in advance through time series trend verification and sharp volatility analysis. Whether it is an unexpected decline in sales revenue or an unreasonable surge in marketing expenses, it can be discovered in the early stage, sounding the alarm for the company, prompting the management to adjust the business strategy in time, such as optimizing sales plans, re-evaluating marketing channels, etc., to avoid further expansion of risks. The target financial data is disassembled and verified from multiple dimensions to ensure that each data item conforms to its due development law. Once an abnormal jump is found and marked, the subsequent auditors can focus on these problem data and conduct in-depth investigations to determine whether it is caused by data entry errors, sudden changes in business processes, or external market shocks, effectively ensuring that the financial data truly reflects the company's operating conditions and providing a reliable basis for decision-making. Based on the precise analysis results provided by the multi-dimensional verification layer, the company can clearly see the dynamic changes in the input and output of each business link in the time dimension. For example, after identifying the invalid marketing cost jump, the part of the expenditure can be cut in time, resources can be transferred to more efficient promotion channels, and the future resource allocation direction can be reasonably planned based on the data trend to improve the overall operational efficiency and achieve the optimal allocation of resources.

[0177] In summary, the multi-dimensional verification layer of the data proofreading model further improves the accuracy and reliability of financial auditing through sophisticated time series trend verification and multi-dimensional correlation analysis.

[0178] In principle, the core operation of the multi-dimensional verification layer of the data proofreading model relies on advanced time series analysis technology, which aims to accurately identify potential problems from complex and changeable financial data and ensure data quality and corporate financial health.

[0179] The multidimensional verification layer groups different subjects, such as "sales revenue", "marketing expenses", "traffic acquisition costs", etc., and makes full use of powerful time series models such as ARIMA (autoregressive moving average model) and LSTM (long short-term memory network). Taking e-commerce companies as an example, for the "sales revenue" subject, first collect historical sales data from the past few years, quarterly or even monthly. These data precipitate the change pattern of the company's sales business over time, whether it is seasonal fluctuations, growth driven by market trends, or ups and downs under the impact of special events, all are recorded one by one. The ARIMA model, with its accurate grasp of the autocorrelation and moving average characteristics of the data, can dig out the stable trend components hidden in the historical data and build a preliminary prediction framework; while the LSTM model, with its effective use of long-term memory information, is particularly good at capturing those complex nonlinear change trends, such as the sharp change pattern of sales data before and after the e-commerce promotion node. The two complement each other. After applying these models to massive historical data training, the sales revenue change trend values ​​of different months in the next quarter can be accurately predicted. For example, based on historical laws and model calculations, it is predicted that sales revenue in January should increase by 10% month-on-month to 5 million yuan. This forecast value carries the dual endorsement of historical experience and the inherent laws of the data.

[0180] When the predicted value is obtained based on the model, the multi-dimensional verification layer immediately starts a rigorous comparison and verification process. Taking "sales revenue" as an example, after the actual business occurs, the actual sales revenue value for January is obtained. If it is only 4 million yuan, it is far from the predicted 5 million yuan. At this time, this inconsistency is quickly locked. Similarly, in the "marketing expenses" account, the model predicts that the marketing expenses of this quarter should rise steadily based on the business expansion rhythm and past investment rules, with an average monthly growth of 8%. However, when the data is actually checked, it is found that the marketing expenses suddenly soared by 50% in a certain month, which deviated greatly from the predicted trend. This sharp contrast immediately triggered an abnormal alarm. Through this detailed comparison, any financial data changes that deviate from the normal trend will be exposed.

[0181] After finding that the actual value did not match the predicted value, the multi-dimensional verification layer further dug into the root cause of the problem and carried out volatility analysis of the account balance. For the 4 million yuan data in January in which the actual value of "sales revenue" did not match the predicted value, not only did we carefully compare the revenue fluctuations of adjacent months to see if there were reasonable fluctuations caused by seasonal or short-term business adjustment factors, but we also extensively referred to the average fluctuation range of the same period in the same industry. With the help of big data, we collected sales data of e-commerce companies in the same region and of the same type. If we found that the sales revenue of surrounding peers generally increased in that month, and the historical fluctuations in the same period had never been so severe, we comprehensively judged that this was likely to be an abnormal jump. For the month when "marketing expenses" soared abnormally, we deeply explored the expense details and analyzed each investment expenditure. We found that it was a huge amount of invalid advertising that caused the cost to soar, and the previous effect of this investment channel was extremely poor. This obviously deviated from the normal marketing investment rhythm and was not within the scope of reasonable fluctuations. Through this multi-dimensional, internal and external combined volatility analysis, we can accurately identify abnormal jumps in account balance changes, providing a solid basis for subsequent audits and decision-making.

[0182] In summary, the multi-dimensional verification layer of the data proofreading model uses cutting-edge time series models and rigorous verification and analysis processes to ensure that financial data follows reasonable trends in all aspects. Both the stable evolution in time series and the logical consistency across subjects and file sources are strictly controlled, becoming the core driving force for financial data quality control, risk warning and operational optimization.

[0183] As an optional embodiment, in 103, the target financial data is grouped according to the corresponding subjects, time series groups, and file source groups through the multi-dimensional verification layer of the data verification model, and time series trend verification and multi-dimensional correlation analysis are performed to obtain a third intermediate verification result, including:

[0184] According to the subject groupings corresponding to each of the target financial data, a multivariate regression analysis is performed on the target financial data to obtain a first historical correlation between different subject groupings and a predicted value of a data item that satisfies the first historical correlation; the degree of deviation between each data item in the target financial data and the predicted value of the data item is compared; an Apriori algorithm is used to extract a second historical correlation between different subject groupings from the target financial data, and it is verified whether the confidence score corresponding to the second historical correlation reaches a set score; and data items in the target financial data whose degree of deviation is greater than a set deviation threshold, or whose confidence score does not reach a set score, are marked as abnormal data items.

[0185] For example, in the multi-dimensional verification layer of the data proofreading model, this structure can dig deep into the internal logic of financial data through diversified and sophisticated analytical methods to ensure the accuracy and reliability of the data.

[0186] The multi-dimensional verification layer conducts multivariate regression analysis based on the subject groups corresponding to the target financial data, such as "operating income", "accounts receivable", "fixed assets", "accumulated depreciation", etc. Taking a manufacturing company as an example, for the two closely related subject groups of "operating income" and "accounts receivable", detailed data covering different business cycles and market environments over the past years are collected. The multivariate regression model deeply explores the complex internal connections. It considers many factors, such as the impact of seasonal sales peaks and troughs on operating income, and the impact of corporate credit policy adjustments on the accounts receivable collection cycle. Through rigorous calculations, the first historical correlation between different subject groups is accurately extracted. For example, under specific market conditions, it is found that for every 10% increase in operating income, accounts receivable will increase by an average of 8%. At the same time, based on this correlation, the model can also generate accurate forecast values ​​for current and future data items. Assuming that based on the existing business trends and historical laws, the forecast value of accounts receivable in a certain period of the next quarter should be 5 million yuan, providing a key reference for subsequent verification.

[0187] After the predicted values ​​of each data item are obtained based on the multivariate regression model, the multidimensional verification layer quickly starts the detailed comparison process. Still taking "operating income" and "accounts receivable" as examples, after the actual business occurs, the actual value of accounts receivable in a certain period is obtained. If it is only 4 million yuan, compared with the predicted 5 million yuan, the degree of deviation is obvious at a glance. By setting a scientific and reasonable deviation calculation method, such as calculating the relative deviation rate, the deviation between each data item and the predicted value can be accurately quantified. Whether it is an increase in operating income but an abnormal decrease in accounts receivable, or an unreasonable fluctuation on the contrary, any data changes that deviate from the predicted track can be keenly captured, lighting a warning red light for in-depth investigation of the problem.

[0188] At the same time, the multi-dimensional verification layer introduces a powerful Apriori algorithm to extract the second historical correlation between different subject groups from the massive target financial data. Taking "fixed assets" and "accumulated depreciation" as examples, the Apriori algorithm combs through years of asset acquisition and depreciation data, and mines out the inherent laws such as the expansion of fixed assets and the steady increase of accumulated depreciation according to the established depreciation policy. Moreover, it is crucial to verify the confidence scores corresponding to these correlations, which is like labeling the mined laws with "reliability". If a certain association rule, such as the correlation between raw material procurement and production cost increase after a new product is put into production, is verified by a large amount of historical data, its confidence score is as high as 90%, indicating that the association is highly reliable; on the contrary, if the confidence of a rule does not reach the set score, such as finding that there is no logical correlation between the change of inventory goods and the change of sales cost in a certain period of time, and the confidence is extremely low, this means that there may be problems with the data. For example, the confidence score is expressed as the following formula:

[0189]

[0190] Based on the above multi-dimensional verification process, the multi-dimensional verification layer will mercilessly mark the data items in the target financial data whose deviation is greater than the set deviation threshold or whose confidence score does not reach the set score as abnormal data items. These abnormal data items may indicate many hidden dangers such as data entry errors, illegal business process operations, and sudden abnormal market shocks. Through precise marking, subsequent audits and analysts can focus on the core of the problem, ensure that the company's financial data truly reflects the overall operation, and provide a solid data foundation for decision-makers.

[0191] In summary, the multi-dimensional verification layer of the data proofreading model relies on cutting-edge multivariate regression and association rule algorithms, combined with a rigorous verification process, to analyze financial data in an intricate manner, whether it is tracing the complex historical relationships between subjects or verifying the compatibility of current data with past rules, thereby further improving data quality.

[0192] It can be understood that the Apriori algorithm is an algorithm for mining frequent item sets and association rules in data, and plays a key role in the multidimensional verification layer of the data proofreading model. The Apriori algorithm is based on a priori principle, that is, if an item set is frequent, then all its subsets must also be frequent. Conversely, if a subset of an item set is infrequent, then the set itself cannot be frequent. For example, in financial data, if "fixed asset acquisition" and "increase in long-term accounts payable" occur frequently at the same time, then their individual occurrences and smaller combinations containing them, such as only "fixed asset acquisition", are also likely to occur frequently. The Apriori algorithm uses this feature to filter out frequent item sets from the candidate item set by scanning the data set multiple times.

[0193] Specifically, first, you need to obtain and organize the target financial data (or other types of data) and present it in the form of transactions. Each transaction contains several data items. Corresponding to the financial scenario, transactions are financial records, and items are different account data or their characteristic values. For example, a financial record transaction related to sales business may contain items such as "sales revenue", "sales expenses", and "accounts receivable", and each item has a corresponding value. Ensure that the data format is unified and complete, remove obvious errors or too much missing data, and lay the foundation for subsequent algorithm operation.

[0194] Traverse the entire data set, count the number of times each item appears, and compare it with the pre-set minimum support threshold. The minimum support threshold is a key parameter that determines whether the frequency of an item set is worthy of attention. It is usually set based on business experience or multiple experiments. For example, when analyzing corporate cost data, if the minimum support is set to 0.3 (which means that the item set must appear at least 30% of the time in the data set), statistics show that the subject "Raw Material Purchase" appears 40 times in 100 financial records, which meets the threshold requirement and is included in the frequent 1-item set.

[0195] Based on frequent 1-item sets, candidate sets of frequent 2-item sets are generated through combination operations. For example, "raw material purchase" and "accounts payable increase" in the frequent 1-item set are combined together, and the data set is scanned again to count the number of occurrences of these candidate 2-item sets, and the frequent 2-item sets that truly meet the minimum support threshold are screened out. This cycle is repeated, and high-order candidate item sets are continuously generated from low-order frequent item sets and screened until new frequent item sets cannot be generated. In this process, pruning optimization is performed using the prior principle, that is, if a subset of an item set is non-frequent, then the item set itself cannot be frequent, which can greatly reduce unnecessary calculations. Association rules are derived from the obtained frequent item sets. For a frequent item set, such as "sales revenue increase, sales expenses increase, accounts receivable increase", multiple rule combinations such as "sales revenue increase → sales expenses increase and accounts receivable increase" and "sales expenses increase → sales revenue increase and accounts receivable increase" are generated through permutations and combinations. For each generated association rule, its confidence is calculated. The confidence is calculated by dividing the number of times the rule's consequent and antecedent appear by the number of times the antecedent appears, which measures the reliability of the rule. Assuming that in the data set, "sales revenue increased" appeared 60 times, and "sales revenue increased, sales expenses increased, and accounts receivable increased" appeared 50 times, then the confidence of the rule "sales revenue increased → sales expenses increased and accounts receivable increased" is. If the confidence of a rule reaches the preset confidence threshold, which is usually between 0.6 and 0.9, depending on business needs, the association rule is considered valid and retained, otherwise it is discarded. Finally, the frequent item sets and valid association rules obtained can be applied to many fields such as data proofreading, market analysis, and customer behavior prediction. In the multidimensional verification layer of the data proofreading model, these results are used to identify data that violates conventional logic. For example, if a financial record shows "a significant increase in production" but according to the association rule "a significant increase in production → a simultaneous increase in raw material consumption and an increase in energy costs", the actual raw material consumption remains the same, the record is marked as abnormal.

[0196] At the same time, in order to deal with the high time complexity of the Apriori algorithm, a variety of optimization strategies can be used. In addition to the reasonable setting of the minimum support threshold to reduce the number of candidate item sets mentioned above, transaction compression technology can also be used to remove transactions that do not contribute to the generation of frequent item sets after each scan of the data set to reduce the size of the data set; or partitioning technology can be used to divide the large data set into multiple small partitions for separate calculations, and then the results are summarized to improve the efficiency of the algorithm and make it better serve actual business needs.

[0197] As an optional embodiment, in 103, the data verification model further includes an anomaly detection layer. Specifically, in 103, after the financial data in different dimensions are verified at multiple levels through the data verification model and the verification result of the target financial data is obtained, it can also be implemented as follows:

[0198] Through the anomaly detection layer, anomaly scores of each data item in the basic verification result and the financial audit result are obtained, and abnormal data points are identified from each data item based on the anomaly scores; the clustering density of each data item in the basic verification result and the financial audit result is monitored to see whether it is lower than a set density threshold, and data items with a clustering density lower than the set density threshold are taken as abnormal data points; an error type distribution corresponding to a data proofreading model is generated based on the abnormal data points; and model parameters and / or audit verification rules of the data proofreading model are adjusted based on the error type distribution.

[0199] In the anomaly detection layer of the data proofreading model, after obtaining the basic verification results and financial audit results, the first step is to assess the anomaly score for each data item. This score is not given arbitrarily, but is based on a rigorous quantitative system. For example, for the balance dimension of the amount of credit and debit in the basic verification results, if the amount of credit and debit of a data item is completely balanced, it will be scored 0 points; if there is a small deviation, it will be scored 0.1-0.5 points according to the degree of deviation according to the pre-set functional relationship; if the deviation seriously deviates from the reasonable range, it will be scored 0.6-1 points. Similarly, in the amount format dimension, the format is completely standardized and scored 0 points, and there is a format error. 0.2 points, and so on, the basic verification problems of each dimension are comprehensively scored for each data item. In terms of financial audit results, if a large data difference is found in the cross-file verification, or a data item with a serious imbalance in the association relationship in the subject verification, a higher anomaly score will be given according to the difference or imbalance. Through this multi-dimensional comprehensive scoring, the degree of abnormal suspicion of each data item can be accurately quantified.

[0200] At the same time, the anomaly detection layer monitors the clustering density of each data item in the basic verification results and financial audit results. The clustering density reflects the degree of aggregation of data items in the feature space. From a technical implementation point of view, clustering algorithms (such as DBSCAN, etc.) are used to cluster similar data items. Assuming that under normal circumstances, financial data of the same type of business will form relatively tight clusters with high clustering density after feature extraction (such as by subject, time series, file source, etc.). When the density of the cluster where a data item is located is lower than the set density threshold, it means that it deviates from the normal data aggregation pattern and is likely to be an abnormal data point. For example, when analyzing sales data for a certain period of time, most of the financial records of normal sales business are clustered into tight clusters based on features such as amount, customer type, and sales region. If a sales record exists in isolation and there are almost no similar data items around it, the clustering density is extremely low, and it is regarded as an abnormal data point.

[0201] Once the abnormal data points are identified, the anomaly detection layer further summarizes and organizes them to generate the error type distribution corresponding to the data proofreading model. Detailed statistics are collected on the distribution of various types of errors in all abnormal data points, such as the proportion of amount format errors, the proportion of loan imbalance errors, and the proportion of account association anomalies. Based on this precise error type distribution, the model parameters and / or audit verification rules of the data proofreading model are adjusted in a targeted manner. If it is found that amount format errors occur frequently, on the one hand, the model parameters for amount format recognition and conversion in the data preprocessing link are adjusted to improve its sensitivity and correction ability for various irregular formats; on the other hand, the audit verification rules are optimized to increase the strictness of the amount format check, increase the frequency of spot checks, or refine the inspection standards. Similarly, if there are many account association anomalies, re-examine the weight parameters of each account association model in the account verification layer, strengthen the verification depth of key related accounts, ensure that the model and rules keep pace with the times, continuously improve the ability to capture abnormal data, and ensure the quality of financial data.

[0202] In summary, the anomaly detection layer injects powerful adaptive adjustment capabilities into the data proofreading model through sophisticated anomaly scoring, cluster density monitoring, and model optimization strategies based on error type distribution, enabling it to always maintain keen anomaly insight in a complex and changing financial data environment, helping corporate financial data management move towards higher accuracy and intelligence.

[0203] Specifically, the anomaly detection layer of the data proofreading model is responsible for accurately identifying potential anomalies in the complex ocean of financial data. Its core operation integrates a variety of data detection algorithms and corresponding strategies to ensure the high-quality execution of data proofreading in all aspects. When the basic verification results and financial audit results are delivered to the anomaly detection layer, the anomaly detection process is immediately started. The Isolation Forest algorithm takes the lead and identifies anomalies based on the isolated characteristics of the data. In principle, the algorithm treats each data point as an independent sample, and constructs multiple isolation trees by randomly selecting features and randomly dividing the value range. Normal data points are similar to each other and are closely clustered, so the paths in these isolation trees are often shorter, while potential abnormal data points will form longer paths in the tree due to deviations from the overall distribution. For example, when analyzing a large amount of expense reimbursement data of an enterprise, normal reimbursement amounts are usually concentrated in a certain range and follow the company's expense standards and business practices. They quickly fall into similar branches in the isolation tree constructed by Isolation Forest; but if there is a record with an extremely high amount, far beyond the reasonable range and unrelated to other reimbursements, the corresponding path in the tree will become significantly longer, and thus be marked as a potential anomaly by the algorithm.

[0204] At the same time, the DBSCAN algorithm operates in parallel. As mentioned above, it identifies anomalies based on the clustering density of data items in the feature space. By extracting key features such as subjects, time series, and file sources, similar financial data are clustered. Taking the company's procurement business data as an example, normal purchase orders have certain similarities in feature dimensions such as amount, supplier, and procurement cycle, and will form tight clusters with high clustering density; once a certain purchase data is far different from the surrounding data in these features, and the density of the cluster is lower than the set threshold, such as a sudden purchase order from an unfamiliar supplier with a huge amount and an unreasonable procurement cycle, the DBSCAN algorithm will accurately capture this abnormal signal and list it as potential problem data.

[0205] For example, using Isolation Forest, we build a random forest to separate data points and identify outliers. The anomaly score for each data point is: Among them, E(h(x)) is the expected value of the expected path length of point x, and c(n) is the normalization coefficient. At the same time, DBSCAN, a density-based clustering algorithm, is also used to monitor points with low density as abnormal points. The core formula of the algorithm is: If the neighborhood density of a point is lower than the set threshold, it is judged as an outlier.

[0206] After initially screening out potential anomalies, the anomaly detection layer further uses the powerful machine learning model of random forest to deepen the analysis and optimize the rules. Random forest consists of multiple decision trees, each of which is trained based on different random subsets of data and has powerful classification and prediction capabilities. The potential abnormal data and the corresponding normal data are input into the random forest model together, and the model learns and judges based on the multi-dimensional characteristics of the data. For example, for the account-related data in the financial audit results, the random forest model learns the normal linkage relationship between accounts in a large amount of past business data, such as "sales revenue growth → corresponding increase in accounts receivable, and sales expenses increase proportionally". When faced with newly emerging suspected abnormal data, such as a sharp increase in sales revenue in a certain period of time but a decrease in accounts receivable, and abnormal changes in sales expenses, the model can accurately judge the possibility of abnormality based on the learned rules.

[0207] Based on the judgment results of the random forest model and the continuous learning of potential abnormal data, the anomaly detection layer dynamically adjusts the model parameters and audit verification rules of the data proofreading model. If a certain type of anomaly is found to occur frequently, such as the confusion of expense reimbursement formats in some areas due to the expansion of business into new areas, the model automatically adjusts the format recognition parameters of the expense data in the data preprocessing link for the area to improve fault tolerance and adaptive conversion capabilities; at the same time, the audit verification rules are optimized to add additional review points for expense reimbursements in new areas, such as focusing on verifying the authenticity of bills and the rationality of expenses. Through this dynamic optimization, the verification logic is closely aligned with business development and data changes, gradually improving adaptability and accuracy in complex scenarios, ensuring accurate and efficient data proofreading, and protecting the quality of corporate financial data.

[0208] In summary, the anomaly detection layer relies on the intelligent collaboration of anomaly detection algorithms such as Isolation Forest and DBSCAN with random forests to achieve a closed-loop system from anomaly discovery to rule optimization, allowing the data proofreading model to be at ease in the complex and changeable field of financial data and always maintain a high degree of intelligence and accuracy.

[0209] Further optionally, a decision tree or random forest model is combined to dynamically adjust the rules according to the verification results introduced in the above embodiment. Among them, the decision function of the random forest is: Among them, T i (x) is the classification result of the i-th decision tree. According to the distribution of error categories predicted by the data proofreading model, the threshold and logic of the verification rules in the data proofreading model are adjusted to optimize the adaptability of the rules, thereby improving the accuracy and stability of the verification. Combining the above steps, multi-dimensional intelligent proofreading of financial data is realized. Specifically, the data verification content corresponding to each data dimension is as follows:

[0210]

[0211] As an optional embodiment, in 104, compliance and risk analysis is performed on the target financial data through a multidimensional analysis model to obtain analysis results of the target financial data.

[0212] Specifically, in 104, first, the corresponding financial indicator data is calculated based on the target financial data through the indicator calculation layer of the multidimensional analysis model; the financial indicator data at least includes: gross profit margin and current ratio. Then, the financial indicator data is multi-dimensionally decomposed through the comparison and evaluation layer of the multidimensional analysis model, and the sub-financial indicator data obtained by the decomposition are evaluated for differences and predicted for relevance from different dimensions to identify abnormal indicator data in the financial indicator data. Then, the abnormal transaction pattern corresponding to the abnormal indicator data in the financial indicator data is determined through the pattern recognition layer of the multidimensional analysis model. Finally, the analysis result is generated based on the financial indicator data and the abnormal transaction pattern through the output layer of the multidimensional analysis model.

[0213] Specifically, in the multidimensional analysis model of step 104, the indicator calculation layer is like a rigorous financial analyst who takes the lead in starting work. It takes the target financial data as the cornerstone and uses the established financial calculation formula to accurately calculate various key financial indicator data. Taking the gross profit margin as an example, the calculation formula is "(sales revenue-sales cost) ÷ sales revenue × 100%". The indicator calculation layer will accurately capture the corresponding sales revenue and sales cost data items from the target financial data, strictly calculate according to the formula, and obtain the accurate gross profit margin value. Similarly, for the current ratio, according to the formula of "current assets ÷ current liabilities", the current assets and current liabilities related data are quickly integrated to calculate the current ratio indicator. These financial indicator data are like the "physical examination indicators" of the company's financial health, reflecting the operating conditions of different aspects of the company and providing basic data support for subsequent in-depth analysis.

[0214] Next, the comparative evaluation layer takes over the financial indicator data and carries out a detailed multi-dimensional decomposition operation. Still taking the gross profit margin as an example, the decomposition is carried out from the product dimension. If the enterprise operates multiple products, the gross profit margin of each product will be calculated separately to see which products are the main contributors to profits and which products have low gross profit margins and have room for optimization; from the time dimension, the gross profit margin changes are compared by quarter and year, and the dynamic trend of the enterprise's profitability is observed, whether it is continuously rising, fluctuating or gradually declining. After completing the multi-dimensional decomposition to obtain the sub-financial indicator data, the comparative evaluation layer quickly starts the difference evaluation link. Compare the gross profit margins of the same product of the enterprise in different periods. If the gross profit margin of a product suddenly drops sharply and exceeds the normal fluctuation range, it is marked as a difference point; at the same time, it is compared with the average gross profit margin of similar products in the same industry. If it is far below the industry average, it is also listed as an abnormal concern object. In addition, the data analysis algorithm is used to predict the correlation of the decomposed sub-indicator data, such as predicting the impact of raw material price fluctuations on product gross profit margins and production cost-related sub-indicators, to detect potential risks in advance, and accurately identify abnormal indicator data in financial indicator data through comprehensive difference evaluation and correlation prediction.

[0215] Once the abnormal indicator data is determined, the pattern recognition layer quickly intervenes to dig deep into the abnormal transaction patterns behind it. Assuming that the company's current ratio continues to decline in a certain period of time, far below the reasonable range, the pattern recognition layer will trace back the corresponding financial data to check whether there are abnormal transactions such as frequent repayments of short-term large loans due, and a sharp decrease in current assets due to unreasonable hoarding or bad debt losses. If the accounts receivable turnover rate drops significantly, combined with sales data, collection records, etc., it is determined whether there are problems such as excessive credit sales and difficulty in collecting payments due to uncontrolled customer credit management. By mining the transaction details associated with various abnormal indicator data, the abnormal transaction patterns are accurately located to reveal the hidden operational risks hidden under the financial appearance for the company.

[0216] Finally, the output layer summarizes the information from all parties and generates the final analysis results based on the complete financial indicator data and the accurately identified abnormal transaction patterns. This analysis result is clear and organized. On the one hand, it lists in detail the data of various financial indicators, including the values ​​and trend changes of conventional indicators, as well as the specific circumstances of abnormal indicator data, such as abnormal gross profit margin product categories, time periods when abnormal current ratios occur, etc. On the other hand, it deeply analyzes abnormal transaction patterns and gives targeted risk warnings and improvement suggestions, such as "the gross profit margin of a certain product continues to decline due to rising raw material procurement costs and intensified market competition. It is recommended to optimize procurement channels and increase product added value" and "the current ratio is too low due to the high short-term debt repayment pressure. Funds should be reasonably planned and accounts receivable collection should be strengthened." Such comprehensive analysis results provide decision-making basis for corporate management, helping companies to adjust their business strategies in a timely manner and ensure stable financial operations.

[0217] In summary, the multidimensional analysis model works together at all levels, from indicator calculation to anomaly mining to result output, to build a rigorous and efficient system for compliance and risk analysis of corporate target financial data, becoming a powerful assistant for corporate financial management.

[0218] As an optional implementation, the multidimensional analysis model aims to conduct in-depth analysis of financial data from multiple dimensions to comprehensively assess the compliance and risk of financial data. The execution of each step in the substantive procedure module echoes and works in coordination with the different levels of the multidimensional analysis model, jointly facilitating more accurate auditing and analysis of financial data.

[0219] On the one hand, in the multidimensional analysis model, the indicator calculation layer plays a fundamental and key role. Similarly, the first step of the indicator calculation of the substantive program module is also the same. By calculating key financial indicators such as gross profit margin and current ratio, just as the multidimensional analysis model calculates various representative financial indicator data based on the target financial data, these indicators can directly reflect different aspects of the company's operations. For example, gross profit margin reflects the profitability of the company, and current ratio reflects the company's short-term debt repayment ability. The comprehensive financial analysis generated based on these indicators is equivalent to the preliminary analysis results based on financial indicator data output by the multidimensional analysis model, which provides important data references for further in-depth comparisons, identification of anomalies, and other links, and is the cornerstone of subsequent in-depth analysis.

[0220] On the other hand, the comparative evaluation layer of the multidimensional analysis model will decompose and analyze the financial indicator data from different dimensions, and the time dimension is an important aspect. The comparison of abnormal data based on time periods in the substantive procedures also focuses on this point, requiring that the extracted historical data and current data are consistent in time periods and have matching financial characteristics. This echoes the idea of ​​the comparative evaluation layer to observe the indicator change trend from the time dimension and compare data from different periods.

[0221] Normal data that is in the same period as the current abnormal data and meets the corresponding financial standards (such as subjects, transaction types, amount ranges, etc.) is filtered out from the system historical data according to specific rules. This process is similar to the multidimensional analysis model comparison evaluation layer. When analyzing, it will accurately locate data under different categories based on various conditions. For example, when analyzing the profitability of each product line of an enterprise, it will distinguish different products (subjects), sales methods (transaction types) and corresponding income ranges (amount ranges) and extract data for comparison. This is also to prepare for a more scientific and reasonable comparative analysis in the future.

[0222] Compare the significant differences between the current abnormal data and the extracted normal data in terms of transaction amount, transaction time, and transaction type, and judge the abnormal situation by setting a threshold. This is consistent with the logic of the multidimensional analysis model comparison evaluation layer to evaluate the difference of each sub-indicator data when identifying abnormal indicator data. For example, the comparison evaluation layer will compare the change in gross profit margin of the same product in different periods, and set a threshold based on a reasonable fluctuation range to determine whether an abnormality has occurred. Here, the abnormality is judged based on the relative difference in amount, the matching of transaction type and time, etc., and the threshold is used to detect situations in the data that do not conform to the normal rules.

[0223] Compared with the comparative analysis in related technologies that focuses more on the differences in single data points, the analysis of the correlation and location of abnormal data added here is consistent with the concept of multidimensional analysis models to deeply explore the internal logic of data and the hidden patterns behind abnormal indicator data. It is a powerful supplement to traditional analysis.

[0224] Using the K-means algorithm to perform cluster analysis, we check whether abnormal data falls into a specific cluster based on the subject, transaction type, amount and other characteristics. This is similar to the multidimensional analysis model pattern recognition layer that analyzes the transaction details associated with abnormal indicator data to discover potential problems. If multiple abnormal data are concentrated in the same cluster, it means that there may be common problems, such as reflecting that the company has systemic risks in a certain type of business process or accounting processing link. This is consistent with the goal of the multidimensional analysis model to grasp the reasons behind data anomalies as a whole.

[0225] Analyze the time distribution of abnormal data to see whether abnormal transactions occur in a specific time period. This is consistent with the idea of ​​the multidimensional analysis model comparison and evaluation layer to gain insight into data change trends and discover abnormal fluctuation patterns from the time dimension. For example, if abnormal transactions are concentrated at the end of the month or the end of the quarter, it may indicate that the company has problems with specific accounting operations such as period-end closing and cost accounting. This helps to discover hidden abnormal patterns from a more macroscopic time perspective, and cooperates with the multidimensional analysis model's function of finding problems from multiple dimensions.

[0226] Similar to the previous embodiment, by analyzing the common patterns of historical data and comparing it with the current abnormal data, it is determined whether the current abnormality conforms to the historical regular change pattern. This process is exactly the same as the multi-dimensional analysis model pattern recognition layer judging the abnormal transaction pattern behind the abnormal indicator based on a large amount of past data learning. Whether it is judging whether the income growth pattern conforms to the seasonal changes in the market, whether the cost changes follow the normal production and operation rules of the enterprise, etc., it is all based on the use of intelligent algorithms to mine the deep laws of the data, so as to accurately identify the abnormalities, and jointly improve the ability to capture abnormal situations with the multi-dimensional analysis model.

[0227] In the multidimensional analysis model, the anomaly detection layer will discover potential anomalies through algorithms such as Isolation Forest, DBSCAN, and models such as random forests, and the anomaly detection in the substantive program module is also based on algorithms. By calculating the anomaly coefficient or comparing the characteristic values ​​to carefully examine the anomalies and deviations in the data, this is based on the characteristics of the data itself and accurately locates the data points that may have problems in a quantitative way. The goal of the anomaly detection layer of the multidimensional analysis model is consistent with that of finding the anomalies hidden in the data as comprehensively and accurately as possible to ensure the quality of financial data.

[0228] For potential anomalies or errors found, the system automatically generates adjustment suggestions, prompting users to manually review or automatically correct them. This is similar to the role of the multidimensional analysis model output layer in giving risk warnings and improvement suggestions based on the analysis results. The multidimensional analysis model output layer will generate corresponding analysis results based on financial indicator data and identified abnormal transaction patterns to provide decision-making basis for enterprises. The adjustment suggestions here are also based on the detected anomalies, providing specific guidance for subsequent data corrections, business adjustments, etc., helping enterprises to better improve financial data, standardize business operations, and ensure financial compliance and robustness.

[0229] To sum up, the various steps of the substantive procedure module are closely integrated with the multidimensional analysis model, which conducts a comprehensive audit and analysis of financial data from different angles and using different methods. The two cooperate and complement each other to help enterprises manage and use financial data with high quality, and promptly discover and resolve potential financial risks and problems.

[0230] In 105, an audit report corresponding to the target financial data is generated based on the verification result and the analysis result, wherein the audit report at least includes: the verification result of the target financial data, compliance assessment, and risk warning.

[0231] Specifically, the verification results of the target financial data are one of the basic contents of the audit report. It presents in detail the specific results obtained after multi-level verification of the target financial data through the data verification model. For example, in the process of data verification, it may be found that some financial data have errors in the amount format, such as irregular writing of numbers, incorrect decimal point position, etc. These specific error information will be clearly listed in the verification results; for example, in the account verification, there may be an imbalance between credits and debits. The report will point out which accounts, in which time period this imbalance occurred, and the specific amount difference of the imbalance, etc. In addition, it will also include abnormal data points identified by the anomaly detection layer and their related information, such as the file where the abnormal data point is located (such as a record in "accounts receivable.xlsx"), the specific business content involved, the anomaly score, etc., so that readers can clearly understand the problems and conditions of the target financial data in the verification link.

[0232] Compliance assessment is an important part of the audit report. It is based on the compliance and risk analysis results of the target financial data conducted by the multidimensional analysis model. First, the target financial data will be comprehensively reviewed based on relevant financial laws and regulations, accounting standards and the internal financial system of the enterprise. For example, check whether the company's revenue recognition complies with the conditions and time points stipulated in the accounting standards, whether there is any situation of early or delayed recognition of revenue to manipulate profits; whether the cost accounting follows the principle of consistency, and whether there is any behavior of arbitrarily changing the cost accounting method to adjust costs. Then, based on the review results, an assessment is conducted to clearly point out in which aspects the target financial data complies with the requirements of laws and regulations and systems, and in which aspects there are compliance issues. For existing compliance issues, the nature of the problem, the amount involved and the possible impact, such as possible tax risks and legal risks, will be explained in detail, providing accurate compliance information for the company's management and relevant stakeholders so that they can take appropriate measures to rectify and improve.

[0233] The risk warning section is a valuable part of the audit report. It also stems from the analysis results of the multidimensional analysis model and the in-depth insight into the financial data during the entire audit process. On the one hand, based on the analysis of financial indicator data, such as the trend and value of gross profit margin, current ratio and other indicators, the company may be prompted to face operational risks. For example, if the gross profit margin continues to decline, it may indicate that the company is facing operational risks such as intensified market competition and rising costs, and needs to pay attention to product pricing strategies and cost control. If the current ratio is too low, it may imply that the company's short-term debt repayment ability is insufficient and there is a risk of capital chain rupture. It is necessary to arrange funds reasonably and optimize the debt structure. On the other hand, combined with the identification results of abnormal transaction patterns, if some abnormal transactions are found to be concentrated in a specific time period or related to specific suppliers and customers, it may indicate the existence of internal control risks and fraud risks. The report will elaborate on the specific manifestations, possible causes and potential impact of these risks in detail, provide forward-looking risk warnings for enterprises, help enterprises formulate risk management strategies in advance, and prevent potential financial and operational risks.

[0234] After completing the verification and analysis of the target financial data, the auditors or related systems will integrate and sort out the verification and analysis results. First, the verification results are classified and summarized, and sorted according to the data error type, abnormal data points and other dimensions to ensure that the information is clear and organized. Then, combined with the various indicators and conclusions of the compliance assessment, as well as the content of the risk warning, professional audit report writing templates and languages ​​are used to organically integrate this information into the audit report. In the writing process, attention will be paid to the logic and readability of the report, so that the content of the report is comprehensive, accurate and easy to understand. Finally, after review and proofreading to ensure that the report is correct, a formal audit report is generated.

[0235] Audit reports are of great significance to enterprises in many aspects. They provide comprehensive and accurate financial information to the management of enterprises, helping them understand the quality and reliability of the financial data of enterprises so as to make scientific and reasonable business decisions. For example, according to the risk warnings in the report, the management can adjust the business strategy in time and strengthen risk management; according to the compliance assessment results, improve the internal control system to ensure the legal and compliant operation of the enterprise. For external stakeholders, such as investors and creditors, the audit report is an important basis for them to understand the financial status and operating results of the enterprise, which helps them to evaluate investment risks and returns and make correct investment and credit decisions. In addition, the audit report also reflects the financial management level and internal control effectiveness of the enterprise to a certain extent, which plays a positive role in enhancing the market image and reputation of the enterprise.

[0236] To sum up, the audit report provides enterprises with a comprehensive and in-depth presentation of financial audit results by integrating the verification results of target financial data, compliance assessments and risk warnings, which has important guiding and reference value for the business management and development of the enterprise.

[0237] In 105, it is mentioned that an audit report corresponding to the target financial data is generated based on the verification results and analysis results, which includes the verification results of the target financial data, compliance assessment, risk warnings, etc. The content described here is a further refinement and extension of the audit report generation and subsequent processing in 105.

[0238] After in-depth proofreading and substantive procedure review, the data has completed a comprehensive inspection and analysis of the financial data. At this time, the compliance export module plays a key role. It is responsible for the final export of these strictly processed data and the generation of audit reports. This module ensures the compliance and accuracy of the data from the processing flow to the final report output, and is an important link between data processing and report generation.

[0239] The system automatically generates an Excel file that complies with financial audit standards. The file may contain detailed financial data tables, which are accurate data after in-depth proofreading and substantive procedures. For example, asset data (such as monetary funds, accounts receivable, etc.), liability data (such as short-term loans, accounts payable, etc.), equity data (such as paid-in capital, surplus reserves, etc.) and profit and loss data (such as operating income, operating costs, etc.) are all presented in a standardized format and accurate values, which is convenient for corporate financial personnel to conduct further data processing and analysis, and also convenient for data interaction and sharing with other financial systems or software.

[0240] The Word summary report will list in detail the abnormal data identified during the audit process, which echoes the verification results mentioned in 105. For example, it may be pointed out that in the "Accounts Receivable.xlsx" file, the amount of accounts receivable of a certain customer exceeds the normal credit period and the amount is large, or in the "Operating Income.xlsx", the confirmation time of a certain income does not comply with accounting standards and other specific abnormal situations, including the file where the abnormal data is located, the specific content, the abnormal form of expression, etc., to provide the company with a clear list of problems for subsequent targeted verification and processing. Based on the analysis and judgment of the abnormal data, the system will automatically generate corresponding adjustment suggestions, which is also an important part of the audit report in 105. For example, for the above-mentioned accounts receivable anomalies, it is recommended that the company communicate with the customer in a timely manner, verify the payment situation, and make provisions for bad debts when necessary; for the abnormal revenue recognition, it is recommended to readjust the revenue recognition time and amount in accordance with the requirements of accounting standards. These adjustment suggestions are highly targeted and operational, and can help companies quickly correct problems in financial data and improve the quality and accuracy of financial data.

[0241] The financial key indicator analysis report covers the analysis of financial key indicators, which is connected with the calculation and analysis of financial indicator data through multidimensional analysis models mentioned in 105. For example, the values, change trends and comparisons with the average level of the same industry of indicators such as gross profit margin, current ratio and debt-to-asset ratio will be analyzed. Through these analyses, enterprises can clearly understand their profitability, debt repayment ability, operational ability and other aspects, discover the advantages and potential problems in the operation of the enterprise, and provide strong data support for the strategic decision-making and operation management of the enterprise.

[0242] Summarize the compliance issues involved in the entire audit process, which corresponds to the compliance assessment part of the audit report in 105. It will clearly state in which aspects the company's financial data complies with financial audit specifications and relevant laws and regulations, in which aspects there are compliance risks or problems, and the impact these problems may have on the company. For example, whether the company's cost accounting method complies with the requirements of accounting standards, whether tax declarations are timely and accurate, whether there are financial operations that violate financial regulations, etc., to provide companies with comprehensive compliance assessment results, help companies strengthen compliance management, and prevent legal risks.

[0243] The report generated by electronic signature verification supports formal verification through electronic signatures, which is of great significance. First, electronic signatures ensure the compliance of reports, indicating that the reports are legally authorized and reviewed, and comply with the relevant requirements and processes of financial audits, which enhances the authority and credibility of reports. Secondly, electronic signatures have legal effect. Once a report is electronically signed, it is equivalent to having legally recognized evidence effect, which is of great value in terms of internal corporate management and external audit archiving. For internal corporate management, electronically signed reports can be used as a basis for decision-making and management reference to ensure that management makes decisions based on accurate and compliant financial information; in terms of audit archiving, electronically signed reports are easy to store and retrieve for a long time, and also meet the requirements of audit institutions and regulatory authorities for the authenticity and integrity of financial reports, providing strong guarantees and support for the financial audit work of enterprises.

[0244] To sum up, the data that has undergone in-depth proofreading and substantive procedure review, the Excel files and Word summary reports generated by the compliance export module, and the function that supports electronic signature verification, together constitute a complete, standardized and efficient financial audit report generation and management system, providing comprehensive, accurate and reliable support and guarantee for the company's financial management and audit work.

[0245] In the technical solution of this application, the traditional model that relies on manual verification and simple rules is abandoned, and a data proofreading model is introduced to automatically identify and process data anomalies. With precise algorithms, the risk of errors caused by human negligence is greatly reduced, while the verification process is significantly accelerated, the operation steps are simplified, the operation difficulty is reduced, and the efficiency and reliability of data proofreading are further improved. With the help of multidimensional analysis models, the financial data association is deeply explored from multiple angles, multi-dimensional comparison and trend analysis are performed, and an in-depth review of financial data is achieved to ensure the accuracy and compliance of the data. Through the automated generation process of audit reports, the cumbersome manual preparation process in related technologies is avoided, which not only reduces the burden on auditors, but also improves audit efficiency with high accuracy, which helps to adapt to the pursuit of modern enterprises for intelligent and compliant processing of financial data.

[0246] In another embodiment of the present application, a financial audit device is also provided. Figure 5 The device comprises the following units:

[0247] The collection unit is configured to obtain the target financial data to be processed from multiple financial data sources; the multiple financial data sources at least include: a financial data system in the target unit and a financial data system of an external cooperation unit;

[0248] The verification unit is configured to group the target financial data according to their respective corresponding data attributes to obtain multidimensional data groups; verify the multidimensional data groups from different dimensions through a data verification model to obtain verification results of the target financial data; the verification results at least include: rule verification results of each data item in the target financial data, abnormal data items in the target financial data that fail to pass the rule verification, and / or warning information corresponding to the abnormal data items; the data verification model is pre-configured with data verification strategies corresponding to different dimensions;

[0249] An evaluation unit is configured to perform compliance and risk analysis on the target financial data through a multidimensional analysis model to obtain analysis results of the target financial data; the analysis results include: at least one of a change trend between the target financial data, abnormal trend risk, financial indicator analysis, and abnormal fluctuation warning information;

[0250] The output unit is configured to generate an audit report corresponding to the target financial data based on the verification result and the analysis result; the audit report at least includes: the verification result of the target financial data, the compliance assessment, and the risk warning.

[0251] Further optionally, the verification unit groups the target financial data according to their respective corresponding data attributes to obtain multidimensional data groups, and is configured to: extract the subject identification, time information, and data source information corresponding to each data item from the target financial data; divide the target financial data into subject groups corresponding to different subjects according to the subject identification corresponding to each data item; divide the target financial data into time series groups corresponding to different time periods according to the time information corresponding to each data item; and divide the target financial data into file source groups corresponding to different data sources according to the data source information corresponding to each data item.

[0252] Further optionally, the verification unit performs multi-level verification on financial data in different dimensions through a data proofreading model to obtain the verification result of the target financial data, and is configured as follows: through the basic audit layer of the data proofreading model, a pre-configured basic financial rule audit strategy is adopted to verify each data item in the target financial data to obtain a basic verification result; wherein the basic financial rule audit strategy is used to proofread data items in at least one of the following dimensions: balance of debit and credit amounts, amount format, amount range, and account number format; through the financial audit layer of the data proofreading model, a cross-dimensional financial proofreading is performed on the target financial data to obtain a financial audit result, so as to realize automated proofreading and verification of the target financial data from different dimensions; through the output layer of the data proofreading model, the verification result is generated using the basic verification result and the financial audit result.

[0253] Further optionally, the verification unit performs cross-dimensional financial verification on the target financial data through the financial audit layer of the data verification model to obtain a financial audit result, and is configured as follows: performing cross-file verification on the target financial data according to file source grouping and subject grouping through the file verification module of the financial audit layer to obtain a first financial audit result; performing cross-subject verification on the target financial data according to subject grouping based on the correlation between each subject through the subject verification module of the financial audit layer to obtain a second financial audit result; performing time series trend verification and multi-dimensional correlation analysis on the target financial data according to their respective corresponding subject groups, time series groups, and file source groups through the multi-dimensional verification module of the financial audit layer to obtain a third financial audit result.

[0254] Further optionally, the verification unit, through the file verification module of the financial audit layer, performs cross-file verification on the target financial data according to the file source grouping and the subject grouping to obtain a first financial audit result, and is configured to: compare the loan data items in the target financial data that come from different file source groups but belong to the same subject grouping to identify and mark the difference data items in the loan data items; identify and mark the redundant data items corresponding to the same financial data in different file source groups according to the subject grouping, time series grouping, and file source grouping corresponding to each of the target financial data; generate a first financial audit result containing difference data items and / or redundant data items, and construct a corresponding cross-file analysis report based on the first financial audit result.

[0255] Further optionally, the verification unit, through the subject verification layer of the data proofreading model, performs cross-subject verification on the target financial data according to the subject grouping according to the correlation between each subject, to obtain a second intermediate verification result, and is configured to: for each data item to be audited, determine the associated subject grouping associated with the subject grouping where the current data item is located; perform linkage verification on the subject grouping where the current data item is located and the associated subject grouping to obtain the correlation relationship between the current data item and the relevant data items in the associated subject grouping;

[0256] The second intermediate verification result is generated based on the association relationship.

[0257] Further optionally, the verification unit, through the multi-dimensional verification layer of the data proofreading model, performs time series trend verification and multi-dimensional correlation analysis on the target financial data according to their respective corresponding subject groups, time series groups, and file source groups to obtain a third intermediate verification result, and is configured to: predict the change trend of the target financial data according to their respective corresponding time series groups, and compare whether the actual value of each data item in each time series group is consistent with the predicted change trend value; perform volatility analysis on data items that are inconsistent between the actual value and the predicted change trend value to identify whether the inconsistent data items belong to abnormal jumps; if they belong to abnormal jumps, mark the identified inconsistent data items as abnormal data items, and mark the corresponding volatility analysis results.

[0258] Further optionally, the verification unit, through the multidimensional verification layer of the data proofreading model, performs time series trend verification and multidimensional correlation analysis on the target financial data according to the respective corresponding subject groups, time series groups, and file source groups to obtain a third intermediate verification result, and is configured to: perform multivariate regression analysis on the target financial data according to the respective corresponding subject groups of the target financial data to obtain a first historical correlation between different subject groups, and a predicted value of a data item that satisfies the first historical correlation; compare the degree of deviation between each data item in the target financial data and the predicted value of the data item; extract the second historical correlation between different subject groups from the target financial data using the Apriori algorithm, and verify whether the confidence score corresponding to the second historical correlation reaches a set score; mark the data items in the target financial data whose degree of deviation is greater than a set deviation threshold, or whose confidence score does not reach a set score, as abnormal data items.

[0259] Further optionally, the data proofreading model also includes an anomaly detection layer; the verification unit, after performing multi-level verification on the financial data under different dimensions through the data proofreading model and obtaining the verification results of the target financial data, is also configured to: obtain the anomaly scores of each data item in the basic verification results and the financial audit results through the anomaly detection layer, and identify abnormal data points from each data item based on the anomaly scores; monitor whether the clustering density of each data item in the basic verification results and the financial audit results is lower than a set density threshold, and take the data items with a clustering density lower than the set density threshold as abnormal data points; generate an error type distribution corresponding to the data proofreading model according to the abnormal data points; and adjust the model parameters and / or audit verification rules of the data proofreading model based on the error type distribution.

[0260] Further optionally, the evaluation unit performs compliance and risk analysis on the target financial data through a multidimensional analysis model to obtain an analysis result of the target financial data, and is configured as follows: calculating corresponding financial indicator data based on the target financial data through the indicator calculation layer of the multidimensional analysis model; the financial indicator data at least includes: gross profit margin, current ratio; performing multidimensional decomposition on the financial indicator data through the comparison and evaluation layer of the multidimensional analysis model, and performing difference evaluation and correlation prediction on the sub-financial indicator data obtained by the decomposition from different dimensions to identify abnormal indicator data in the financial indicator data; determining abnormal transaction patterns corresponding to the abnormal indicator data in the financial indicator data through the pattern recognition layer of the multidimensional analysis model; and generating the analysis result based on the financial indicator data and the abnormal transaction pattern through the output layer of the multidimensional analysis model.

[0261] Further optionally, the device also includes a cleaning unit, which is configured to: after obtaining the target financial data to be processed from multiple financial data sources, perform data cleaning processing on the target financial data; identify the data source of each data item in the cleaned target financial data; for each data item, call the format conversion branch model corresponding to the current data item based on the identified data source, and perform format conversion processing on the current data item through the called format conversion branch model to obtain the target financial data in a unified format; wherein the multiple format conversion branch models correspond one-to-one to the multiple financial data sources, respectively, and the conversion parameters in each format conversion branch model are obtained by adaptive learning based on the data encoding features of the multiple financial data sources.

[0262] The system can implement various steps in the above method embodiment, which will not be expanded here.

[0263] See also Figure 6 , Figure 6 This is a schematic diagram of an electronic device provided in an embodiment of the present application. Figure 6 As shown, an embodiment of the present application provides an electronic device 500, including a memory 510, a processor 520, and a computer program 511 stored in the memory 510 and executable on the processor 520. When the processor 520 executes the computer program 511, the financial audit method in the above embodiment is implemented.

[0264] See also Figure 7 , Figure 7 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present application. Figure 7 As shown, this embodiment provides a computer-readable storage medium 600 on which a computer program 611 is stored. When the computer program 611 is executed by a processor, the financial audit method in the above embodiment is implemented.

[0265] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0266] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0267] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A financial audit method, characterized in that: The method comprises: Acquire the target financial data to be processed from multiple financial data sources; the multiple financial data sources at least include: the financial data system in the target unit and the financial data system of the external cooperation unit; Grouping the target financial data according to their corresponding data attributes to obtain multidimensional data groups; Verifying the multidimensional data grouping from different dimensions through the data verification model to obtain verification results of the target financial data; the verification results at least include: rule verification results of each data item in the target financial data, abnormal data items in the target financial data that fail to pass the rule verification, and / or warning information corresponding to the abnormal data items; the data verification model is pre-configured with data verification strategies corresponding to different dimensions; By using a multidimensional analysis model, compliance and risk analysis is performed on the target financial data to obtain analysis results of the target financial data; the analysis results include: at least one of a change trend between the target financial data, abnormal trend risk, financial indicator analysis, and abnormal fluctuation warning information; Based on the verification results and the analysis results, an audit report corresponding to the target financial data is generated; the audit report at least includes: the verification results of the target financial data, compliance assessment, and risk warnings.

2. The financial audit method according to claim 1, characterized in that: The target financial data is grouped according to the corresponding data attributes to obtain multi-dimensional data groups, including: Extracting the subject identification, time information, and data source information corresponding to each data item from the target financial data; According to the subject identification corresponding to each data item, the target financial data is divided into subject groups corresponding to different subjects; According to the time information corresponding to each data item, the target financial data is divided into time series groups corresponding to different time periods; According to the data source information corresponding to each data item, the target financial data is divided into file source groups corresponding to different data sources.

3. The financial audit method according to claim 2, characterized in that: The multi-level verification of the financial data in different dimensions is performed by the data verification model to obtain the verification result of the target financial data, including: Through the basic audit layer of the data verification model, a pre-configured basic financial rule audit strategy is used to verify each data item in the target financial data to obtain a basic verification result; wherein the basic financial rule audit strategy is used to verify data items of at least one of the following dimensions: balance of debit and credit amounts, amount format, amount range, and account number format; Through the financial audit layer of the data proofreading model, the target financial data is subjected to cross-dimensional financial proofreading to obtain financial audit results, so as to realize automated proofreading and verification of the target financial data from different dimensions; The verification result is generated by the output layer of the data verification model using the basic verification result and the financial audit result.

4. The financial audit method according to claim 3, characterized in that: The financial audit layer of the data proofreading model performs cross-dimensional financial proofreading on the target financial data to obtain financial audit results, including: Through the file verification module of the financial audit layer, the target financial data is cross-file verified according to the file source grouping and the subject grouping to obtain a first financial audit result; Through the subject verification module of the financial audit layer, the target financial data is grouped according to the subject and cross-subject verification is performed according to the correlation between the subjects to obtain the second financial audit result; Through the multi-dimensional verification module of the financial audit layer, the target financial data is grouped according to their corresponding subjects, time series groups, and file source groups, and time series trend verification and multi-dimensional correlation analysis are performed to obtain the third financial audit result.

5. The financial audit method according to claim 4, characterized in that: The file verification module of the financial audit layer performs cross-file verification on the target financial data according to the file source grouping and the subject grouping to obtain the first financial audit result, including: Comparing loan data items from different file source groups but belonging to the same account group in the target financial data to identify and mark different data items in the loan data items; According to the subject groups, time series groups, and file source groups corresponding to the target financial data, identifying and marking redundant data items corresponding to the same financial data in different file source groups; A first financial audit result including difference data items and / or redundant data items is generated, and a corresponding cross-file analysis report is constructed based on the first financial audit result.

6. The financial audit method according to claim 3, characterized in that: The subject verification layer of the data verification model performs cross-subject verification on the target financial data according to the subject grouping according to the correlation between the subjects, and obtains the second intermediate verification result, including: For each data item to be audited, determine the associated subject grouping associated with the subject grouping in which the current data item is located; The subject grouping of the current data item and the associated subject grouping are linked and verified to obtain the association relationship between the current data item and the related data items in the associated subject grouping; The second intermediate verification result is generated based on the association relationship.

7. The financial audit method according to claim 3, characterized in that: The multi-dimensional verification layer of the data verification model performs time series trend verification and multi-dimensional correlation analysis on the target financial data according to the corresponding subject groups, time series groups, and file source groups, and obtains a third intermediate verification result, including: Predicting the change trend of the target financial data according to the corresponding time series groups, and comparing the actual value of each data item in each time series group with the predicted change trend value to see whether they are consistent; Perform volatility analysis on data items that are inconsistent between actual values ​​and predicted change trend values ​​to identify whether the inconsistent data items are abnormal jumps; If it is an abnormal jump, the identified inconsistent data item is marked as an abnormal data item, and the corresponding volatility analysis result is marked.

8. The financial audit method according to claim 3, characterized in that: The multi-dimensional verification layer of the data verification model performs time series trend verification and multi-dimensional correlation analysis on the target financial data according to the corresponding subject groups, time series groups, and file source groups, and obtains a third intermediate verification result, including: According to the subject groups corresponding to the target financial data, a multivariate regression analysis is performed on the target financial data to obtain first historical correlations between different subject groups and predicted values ​​of data items satisfying the first historical correlations; Comparing the degree of deviation between each data item in the target financial data and the predicted value of the data item; Using the Apriori algorithm to extract the second historical correlation between different subject groups from the target financial data, and verifying whether the confidence score corresponding to the second historical correlation reaches a set score; Marking data items in the target financial data whose degree of deviation is greater than a set deviation threshold, or whose confidence score does not reach a set score, as abnormal data items; The data proofreading model also includes an anomaly detection layer; After the financial data in different dimensions are verified at multiple levels by the data verification model to obtain the verification result of the target financial data, the method further includes: Obtaining, through the anomaly detection layer, anomaly scores of each data item in the basic verification result and the financial audit result, and identifying abnormal data points from each data item based on the anomaly scores; Monitor whether the clustering density of each data item in the basic verification result and the financial audit result is lower than a set density threshold, and take the data items whose clustering density is lower than the set density threshold as abnormal data points; Generate the error type distribution corresponding to the data proofreading model based on the abnormal data points; Model parameters and / or audit verification rules of the data proofreading model are adjusted based on the error type distribution.

9. The financial audit method according to claim 1, characterized in that: The compliance and risk analysis of the target financial data is performed by the multidimensional analysis model to obtain the analysis results of the target financial data, including: Calculate corresponding financial indicator data based on the target financial data through the indicator calculation layer of the multidimensional analysis model; the financial indicator data at least includes: gross profit rate and current ratio; Through the comparison and evaluation layer of the multidimensional analysis model, the financial indicator data is multidimensionally decomposed, and the sub-financial indicator data obtained by the decomposition are evaluated for differences and predicted for relevance from different dimensions to identify abnormal indicator data in the financial indicator data; Determining the abnormal transaction pattern corresponding to the abnormal indicator data in the financial indicator data through the pattern recognition layer of the multidimensional analysis model; The analysis result is generated based on the financial indicator data and the abnormal transaction pattern through the output layer of the multidimensional analysis model.

10. A financial auditing device, characterized in that: The device comprises the following units, wherein: The collection unit is configured to obtain the target financial data to be processed from multiple financial data sources; the multiple financial data sources at least include: a financial data system in the target unit and a financial data system of an external cooperation unit; The verification unit is configured to group the target financial data according to their respective corresponding data attributes to obtain multidimensional data groups; verify the multidimensional data groups from different dimensions through a data verification model to obtain verification results of the target financial data; the verification results at least include: rule verification results of each data item in the target financial data, abnormal data items in the target financial data that fail to pass the rule verification, and / or warning information corresponding to the abnormal data items; the data verification model is pre-configured with data verification strategies corresponding to different dimensions; An evaluation unit is configured to perform compliance and risk analysis on the target financial data through a multidimensional analysis model to obtain analysis results of the target financial data; the analysis results include: at least one of a change trend between the target financial data, abnormal trend risk, financial indicator analysis, and abnormal fluctuation warning information; The output unit is configured to generate an audit report corresponding to the target financial data based on the verification result and the analysis result; the audit report at least includes: the verification result of the target financial data, the compliance assessment, and the risk warning.

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