Enterprise audit offline risk quantitative evaluation method based on big data

Abstract

The invention relates to the technical field of enterprise auditing, and particularly discloses an enterprise auditing off-line risk quantitative evaluation method based on big data, which comprises the following steps: step 1, constructing a risk model, analyzing a large number of auditing cases by professional auditing personnel through off-line software, and summarizing abnormal data analysis thoughts and methods; according to the method, an offline database is established by adopting a lightweight database, a big data technology is combined, large-scale hardware investment is not needed, and through risk model construction and script program conversion, the software development complexity and cost are reduced, the development period is shortened, the risk model is continuously perfected through expert experience, and new risks are adapted; big data acquisition and modular analysis can flexibly respond to different businesses and auditing scenes, flexible change of an auditing period is realized, offline risk quantitative evaluation and a big data technology are adopted, the attack risk of online data is reduced, data security protection is emphasized, and the security of sensitive data of an enterprise is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise auditing, and in particular to an offline risk quantitative assessment method for enterprise auditing based on big data. Background Art

[0002] In the complex field of internal auditing, there are universal risks with certain commonalities, including financial risks, compliance risks, operational risks and many other aspects. At the same time, due to the unique business models, organizational structures and market positioning of different enterprises, there are also unique risks specific to each enterprise. With the advent of the big data era, the methods and means of internal auditing should have undergone major changes. However, the current large-scale online auditing system has exposed a series of serious defects in actual application.

[0003] First, such systems usually require huge investment, from the purchase of hardware equipment to the development of software systems, and then to subsequent maintenance and upgrades. The accumulated costs constitute a heavy economic burden for many companies.

[0004] Second, the development cycle of large online audit systems is often long, which makes it difficult for them to quickly respond to the dynamic changes in the internal audit needs of enterprises. In today's fast-growing business environment, the business model and risk status of enterprises may change at any time, and online audit systems with long development cycles cannot keep up with these changes in time, causing audit work to lag behind the evolution and extension of risks.

[0005] Furthermore, large-scale online audit systems are weak in supporting the rapid evolution and extension of audit risks. As corporate business continues to expand and the market environment continues to change, new risks continue to emerge like mushrooms after rain. At the same time, flexible changes in audit cycles are also one of the key requirements of internal audits in enterprises. Different business projects and audit scenarios often require audit cycles of different lengths, and large-scale online audit systems are usually unable to meet this flexibility requirement. In addition, the security issue of online use of corporate data is even more urgent. In the context of big data, internal audits of enterprises involve a large amount of sensitive data, such as financial data, customer information, etc. Large-scale online audit systems are very likely to face security risks such as data leakage and hacker attacks. Once the data security line is breached, the enterprise will suffer immeasurable losses. Summary of the invention

[0006] The purpose of the present invention is to provide a method for quantitatively assessing offline risks of enterprise audits based on big data to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above objectives, in a first aspect, the present invention provides a method for quantitatively assessing offline risks of enterprise audits based on big data, comprising the following steps:

[0008] Step 1: Construct a risk model. In the daily internal audit practice of enterprises, professional auditors conduct in-depth analysis of various risk situations. Through careful research on a large number of audit cases, they summarize the unique ideas and scientific methods of abnormal data analysis, and then carefully construct a comprehensive risk model. The model covers the positioning of risk control points and clearly points out the key links where potential risks may occur; conducts accurate risk descriptions and clearly explains the specific characteristics of various risks; formulates rigorous audit procedures to ensure that the audit work is carried out in an orderly manner; provides detailed audit methods to provide specific guidance for actual operations; establishes a list verification procedure for abnormal risk data analysis results to ensure the accuracy of the results; integrates rich reference basic data, uses offline big data technology to widely collect relevant data resources, and continuously enriches and improves the data basis of the risk model. At the same time, it deeply explores detailed information that is prone to risks so as to take preventive measures in advance. Through these efforts, it is ensured that the risk model can accurately and comprehensively reflect the various potential risks faced by the enterprise, providing a solid foundation for subsequent audit work;

[0009] Step 2: Script program conversion. Based on the strict logical structure of the risk model, the script program is converted using advanced programming technology. In this process, the offline risk quantitative assessment method of enterprise audit based on big data is fully introduced, and the powerful technology of big data analysis is deeply integrated into the program script. The audit procedures and detailed audit methods in the risk model are efficiently processed. This processing method can achieve fast data calculation and accurate analysis results, and provide strong technical support for subsequent risk assessment. By converting the risk model script program, the audit work is more standardized and automated, and the work efficiency and accuracy are improved;

[0010] Step 3: Establish an offline database. Use a lightweight database to establish a stable offline database. At the same time, closely combine big data technology to achieve data collection anytime and anywhere. In today's digital age, data from various business links within the enterprise can be easily collected, including critical financial data, complex business process data, and personnel management data. In order to ensure that the collected data is accurate, complete, and reliable, strict data collection standards and specifications are formulated. These standards and specifications clarify the specific scope of data collection, reasonable frequency, and standardized format requirements, so that data collection work has rules to follow, and the collected data is accurately imported into the audit offline database according to the predetermined rules, preparing for subsequent data analysis and processing;

[0011] Step 4: Formulate data collection rules. Clearly and meticulously determine the key requirements for data collection, such as the scope, frequency, and format. Formulate detailed data collection rules. According to the unique business characteristics and specific risk conditions of the enterprise, accurately determine the type of data and reliable sources that need to be collected. Through in-depth analysis of the enterprise's business processes and risk distribution, ensure that the collected data is comprehensive and targeted, and can truly reflect the actual situation of the enterprise. At the same time, establish a sound data quality monitoring mechanism to monitor and strictly verify the collected data in real time. Once errors and abnormalities in the data are found, they can be corrected in a timely manner to ensure the accuracy and reliability of the data, and provide a high-quality data foundation for subsequent audit work;

[0012] Step 5: Offline data processing. In the offline database, in strict accordance with the logical requirements of the risk model, the imported basic data is comprehensively preprocessed, including data cleaning to remove noise and erroneous information in the data to ensure the purity of the data; trimming operations to remove irrelevant data and improve the pertinence of the data; integration operations to organically integrate data from different sources to form a unified data entity. In this process, big data technologies are fully utilized, such as data mining, machine learning and other advanced means to automatically identify and process abnormal data. Through the application of these technologies, the quality and availability of data are improved, providing high-quality intermediate data for data analysis of risk models;

[0013] Step 6: Application of risk quantification assessment method. Use the enterprise audit offline risk quantification assessment method based on big data to conduct in-depth analysis of the pre-processed data. According to the scientific indicator system of the risk model, determine the dimensions and reasonable weights of risk assessment. Use advanced data analysis algorithms and models to conduct quantitative assessment of the data, accurately determine the size and impact of the risk. At the same time, based on the actual situation of the enterprise, formulate a corresponding risk warning mechanism, monitor data changes in real time, and timely discover and warn of potential risks, providing strong support for the enterprise's risk management.

[0014] Step 7: Offline modular program script analysis: Apply the created risk model to analyze offline modular program scripts, conduct further analysis and verification of audit data, and realize multi-angle and multi-level analysis of data through flexible combination and calling of different modules. This analysis method can improve the accuracy and reliability of analysis and ensure a comprehensive grasp of risks. At the same time, the performance and function of program scripts are continuously optimized to improve analysis efficiency and quality. By continuously improving program scripts, they can better adapt to the audit needs of enterprises and provide more powerful tools for risk assessment.

[0015] Step 8: Generate a list of abnormal risk results. Based on the accurate results of risk quantification assessment, generate a detailed list of abnormal risk data analysis results. The list clearly lists potential risk points, risk levels, and related audit recommendations, providing auditors with clear work directions and priorities so that they can carry out audit work in a targeted manner. At the same time, the list is classified and organized for easy access and analysis by auditors. Through clear classification and organization, the efficiency and accuracy of audit work can be improved.

[0016] Step 9: Export the result list. Safely export the abnormal risk data analysis result list from the database and save it in Excel format or other formats that are easy to access and analyze. At the same time, establish a management mechanism for the result list to regularly update and maintain the list to ensure its accuracy and timeliness, provide a reliable basis for the company's decision-making, and ensure that the result list is always kept up to date through an effective management mechanism to provide continuous support for audit work;

[0017] Step 10: Big data sharing. Utilize big data technology to push and share result lists. Auditors can view risk assessment results anytime and anywhere, and communicate and collaborate with other departments in a timely manner. At the same time, establish an offline data platform to realize offline data sharing and collaborative work among departments within the enterprise, improve the enterprise's risk management level and efficiency, and promote the overall development of the enterprise. Through the offline data platform, break down data barriers between departments, realize the circulation and sharing of information, and at the same time, it is not connected to the external network, and the overall offline state is more secure, providing more comprehensive support for the enterprise's risk management.

[0018] Furthermore, the basic data in step one specifically includes internal business data of the enterprise, external relevant data of the enterprise and historical audit data, wherein the historical audit data refers to relevant information and data of the internal audit, comprehensive audit and special audit of the enterprise in previous years. The historical audit data can provide reference and reference for the current risk assessment, help discover the long-term or recurring risk problems that may exist in the enterprise, and evaluate the effectiveness of the enterprise's risk management measures and improvement directions.

[0019] Furthermore, the internal enterprise data specifically refers to financial data, business process data and personnel management data. The financial data specifically refers to data on the enterprise's assets, liabilities, income, costs, profits, etc. This data can improve the accuracy of assessing the enterprise's financial status, operating results and financial risk assessment.

[0020] Furthermore, the business process data specifically refers to the specific process information of each business link of the enterprise, namely, data such as procurement and accounts payable process, production cycle process, sales and accounts receivable process, inventory and warehousing process, marketing management process, etc. Through the analysis of these business process data, we can have an in-depth understanding of the operation of the enterprise's business and discover potential risk points and weak links in the process.

[0021] Furthermore, the external relevant data of the enterprise specifically include industry data, market data and macroeconomic data, among which the industry data include data on the market size, market growth rate, competitive landscape, industry trends, industry policies, etc. of the industry. By comparing the performance of the enterprise with that of the industry as a whole, it is possible to assess the position and competitiveness of the enterprise in the industry, as well as the risks that changes in the industry environment may bring to the enterprise.

[0022] Furthermore, the market data specifically refers to data such as market supply and demand, product price fluctuations, changes in consumer demand, and market share. Market data can reflect the performance of a company's products or services in the market and the market's recognition of the company, helping the company identify market risks and further improve the accuracy of risk assessment.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] First, in the present invention, this method uses a lightweight database to establish an offline database, combined with big data technology, without large-scale hardware investment, by building a risk model and converting it into a script program, reducing the complexity and cost of software development, shortening the development cycle, and continuously improving the risk model based on expert experience to adapt to new risks. Big data collection and modular analysis can flexibly respond to different business and audit scenarios, realize flexible changes in the audit cycle, and use offline risk quantification assessment and big data technology to reduce the risk of online data being attacked, focus on data security protection, and ensure the security of sensitive enterprise data;

[0025] Secondly, in the present invention, the risk model constructed by professional auditors covers many aspects, accurately reflects the potential risks of the enterprise, integrates rich reference data, makes the risk model more scientific and practical, converts script programs, introduces offline big data technology to efficiently process audit procedures and methods, big data ensures data quality, preprocessing improves availability, modular analysis improves accuracy from multiple angles, risk quantification assessment determines risk dimensions and weights, warns of potential risks, generates a list of results to provide direction for the audit, and the export and management mechanism ensures accuracy and timeliness. Big data sharing promotes departmental collaboration and improves the level of enterprise risk management. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] See also Figure 1 In an embodiment of the present invention, a method for quantitatively assessing offline risks of enterprise audit based on big data includes the following steps:

[0029] Step 1: Construct a risk model. In the daily practice of internal auditing in enterprises, professional auditors conduct in-depth and comprehensive analysis of various risk situations. They conduct meticulous research on a large number of audit cases, carefully summarize the unique ideas and scientific and effective methods of abnormal data analysis, and then build a comprehensive, systematic and rigorous risk model. The model covers multiple key aspects. Among them, the positioning of risk control points can clearly point out the key links where potential risks may occur, just like accurately locating the potential outbreak points of risks in a complex enterprise operation network. The risk description is extremely accurate, and the specific characteristics of various risks are clearly and detailedly explained, making the risk as if placed under a microscope, and its shape and characteristics are clear at a glance. At the same time, a rigorous audit procedure is formulated to ensure the audit The work can be carried out in an orderly manner, just like paving a standardized and orderly track for the audit work, and providing detailed audit methods to provide specific and practical guidance for actual operations, just like equipping auditors with precise tools, setting up a list verification procedure for abnormal risk data analysis results, strictly ensuring the accuracy of the results, and adding a solid safety lock to risk assessment. In addition, it integrates rich reference basic data, widely collects relevant data resources with the help of offline big data technology, and continuously enriches and improves the data foundation of the risk model. In this process, it deeply explores detailed information that is prone to cause risks, so as to prepare preventive measures in advance. Through these unremitting efforts, it ensures that the risk model can accurately and comprehensively reflect the various potential risks faced by the enterprise, laying a solid foundation for subsequent audit work;

[0030] The basic data specifically includes the company's internal business data, external relevant data and historical audit data. The historical audit data is the relevant information and data of the company's internal audit, comprehensive audit and special audit in previous years. These historical audit data are like a valuable experience treasure house, which can provide valuable reference and reference for current risk assessment, help discover the company's possible long-term or recurring risk problems, and evaluate the effectiveness of the company's risk management measures and clarify the direction of improvement;

[0031] Internal data of an enterprise refers to financial data, business process data and personnel management data. Financial data refers to data on the assets, liabilities, income, costs, profits and other aspects of an enterprise. These data are like a barometer of the financial status of an enterprise, which can greatly improve the accuracy of assessing the financial status, operating results and financial risk of an enterprise.

[0032] Business process data refers to the specific process information of each business link of the enterprise, namely, data on procurement and payables process, production cycle process, sales and receivables process, inventory and warehousing process, marketing management process, etc. Through in-depth analysis of these business process data, it is like going deep into the operational context of the enterprise, clearly understanding the operation of the enterprise's business, and accurately discovering potential risk points and weak links in the process;

[0033] The external data of an enterprise include industry data, market data and macroeconomic data. The industry data includes data on the market size, market growth rate, competition pattern, industry trends, industry policies, etc. of the industry. By comparing the performance of the enterprise with that of the industry as a whole, it is like putting the enterprise on the big stage of the industry for examination. This can assess the position and competitiveness of the enterprise in the industry, as well as the risks that changes in the industry environment may bring to the enterprise.

[0034] Market data specifically refers to data on market supply and demand, product price fluctuations, changes in consumer demand, market share, etc. Market data can truthfully reflect the performance of a company's products or services in the market and the market's recognition of the company, helping the company to keenly identify market risks and further improve the accuracy of risk assessment;

[0035] Step 2: Script program conversion. Based on the strict logical structure of the risk model, the script program is converted using advanced programming technology. In this process, the offline risk quantitative assessment method of enterprise audit based on big data is fully introduced, and the powerful technology of big data analysis is deeply integrated into the program script. For example, the distributed computing power of big data is used to efficiently process the audit procedures and detailed audit methods in the risk model. This processing method is like installing a powerful engine for the audit work, which can achieve fast data calculation and accurate analysis results, and provide strong technical support for subsequent risk assessment. By converting the risk model script program, the audit work is made more standardized and automated, just like creating an efficient operation system for the audit work, improving work efficiency and accuracy.

[0036] Step 3: Establish an offline database. Use a lightweight database to establish a stable offline database. At the same time, closely combine big data technology to achieve data collection anytime and anywhere. In today's digital age, data from various business links within the enterprise can be easily collected, including critical financial data, complex business process data, and personnel management data. In order to ensure that the collected data is accurate, complete, and reliable, strict data collection standards and specifications are formulated. These standards and specifications clarify the specific scope of data collection, reasonable frequency, and standardized format requirements, so that data collection work has rules to follow, just like drawing an accurate blueprint for data collection work, and accurately importing the collected data into the audit offline database according to predetermined rules, making full preparations for subsequent data analysis and processing, just like reserving sufficient ammunition for audit work;

[0037] Step 4: Formulate data collection rules. Clearly and meticulously determine the key requirements for data collection, such as the scope, frequency, and format. Formulate detailed data collection rules. According to the unique business characteristics and specific risk conditions of the enterprise, accurately determine the type of data and reliable sources that need to be collected. Through in-depth analysis of the enterprise's business processes and risk distribution, ensure that the collected data is comprehensive and targeted, and can truly reflect the actual situation of the enterprise, just like drawing an accurate data portrait for the enterprise. At the same time, establish a sound data quality monitoring mechanism to monitor and strictly verify the collected data in real time. Once errors and abnormalities are found in the data, they can be corrected in a timely manner to ensure the accuracy and reliability of the data, providing a high-quality data foundation for subsequent audit work, just like building a solid data building for audit work;

[0038] Step 5: Offline data processing. In the offline database, in strict accordance with the logical requirements of the risk model, the imported basic data is comprehensively preprocessed, including data cleaning, removing noise and erroneous information in the data, and ensuring the purity of the data. It is like performing a deep processing on the data, performing a cropping operation, removing irrelevant data, and improving the pertinence of the data. It is like performing a precise pruning and integration operation on the data, organically integrating data from different sources to form a unified data whole, just like piecing together scattered puzzle pieces into a complete picture. In this process, make full use of big data technologies, such as data mining, machine learning and other advanced means, to automatically identify and process abnormal data. Through the application of these technologies, the quality and availability of data are improved, and high-quality intermediate data is provided for the data analysis of the risk model, just like creating a sharp sword for risk assessment.

[0039] Step 6: Application of risk quantification assessment method. Use the enterprise audit offline risk quantification assessment method based on big data to conduct in-depth analysis of the pre-processed data. According to the scientific indicator system of the risk model, determine the dimensions and reasonable weights of risk assessment, just like building a precise coordinate system for risk assessment. Use advanced data analysis algorithms and models to quantify the data and accurately determine the size and impact of the risk, just like using precision instruments to measure the scale of the risk. At the same time, combined with the actual situation of the enterprise, formulate a corresponding risk warning mechanism, through real-time monitoring of data changes, timely discover and warn of potential risks, and provide strong support for the enterprise's risk management, just like installing a sensitive risk alarm for the enterprise;

[0040] Step 7: Offline modular program script analysis. Apply the created risk model to analyze offline modular program scripts, conduct further analysis and verification of audit data, and realize multi-angle and multi-level analysis of data through flexible combination and calling of different modules, just like observing a mountain from different perspectives, so as to have a more comprehensive understanding of its shape and characteristics. This analysis method can improve the accuracy and reliability of analysis and ensure a comprehensive grasp of risks, just like using a fine net to capture every detail of the risk. At the same time, the performance and function of the program script are continuously optimized to improve the analysis efficiency and quality, just like injecting continuous power into the program script, so that it can better adapt to the audit needs of the enterprise and provide a more powerful tool for risk assessment.

[0041] Step 8: Generate a list of abnormal risk results. Based on the accurate results of risk quantification assessment, generate a detailed list of abnormal risk data analysis results. The list clearly lists potential risk points, risk levels, and related audit recommendations, providing auditors with clear work directions and priorities so that they can carry out audit work in a targeted manner, just like providing auditors with a clear navigation map. At the same time, the list is classified and organized for easy access and analysis by auditors. Through clear classification and organization, the efficiency and accuracy of audit work can be improved, just like organizing messy items in an orderly manner for easy search and use.

[0042] Step 9: Export the result list. Safely export the abnormal risk data analysis result list from the database and save it in Excel format or other formats that are easy to access and analyze. At the same time, establish a management mechanism for the result list, regularly update and maintain the list to ensure its accuracy and timeliness, and provide a reliable basis for the company's decision-making, just like providing an accurate intelligence for the company's decision-making. Through an effective management mechanism, ensure that the result list is always kept up to date, provide continuous support for the audit work, just like providing a continuous source of power for the audit work;

[0043] Step 10: Big data sharing. Using big data technology, the result list can be pushed and shared. Auditors can view risk assessment results anytime and anywhere, and communicate and collaborate with other departments in a timely manner. At the same time, an offline data platform is established to realize offline data sharing and collaborative work among departments within the enterprise, improve the enterprise's risk management level and efficiency, and promote the overall development of the enterprise. It is like creating a closely cooperative team for the enterprise. Through the offline data platform, the data barriers between departments are broken, and the circulation and sharing of information are realized. At the same time, it is not connected to the external network, and the overall offline state is more secure, providing more comprehensive support for the enterprise's risk management, just like weaving a tight protective net for the enterprise's risk management.

[0044] Table 1: Example of risk model data comparison;

[0045]

[0046]

[0047] Table 2: Offline modular program script data comparison example;

[0048]

[0049] Table 3: Comparison example of basic data for big data collection:

[0050]

[0051]

[0052] Table 4: Data collection rules data comparison example

[0053]

[0054] Table 5: Example of risk quantitative assessment data comparison

[0055]

[0056] This method uses a lightweight database to build an offline database, and closely combines big data technology. In this way, there is no need for large-scale hardware investment, which greatly reduces the economic burden of the enterprise. On this basis, professional auditors use their rich experience to refine the risk model, and then convert it into a script program. In this process, the introduction of expert experience is like injecting a strong impetus into the construction of the basic framework, which can quickly build a stable infrastructure. Moreover, the development of offline modular program scripts shows a high degree of flexibility and can be gradually improved and optimized according to the changes in the actual needs of the enterprise. Compared with the traditional method, this method significantly shortens the software development cycle, enabling enterprises to respond to the dynamic changes in internal audit needs more agilely;

[0057] The risk model is continuously enriched and improved through expert experience, making it highly adaptable. When new risks quietly emerge, the risk model can respond quickly and accurately capture the changes in potential risks. At the same time, the big data collection method of basic data is efficient and convenient, and combined with offline modular program script analysis, it can quickly respond to the diverse needs of different business projects and audit scenarios. Whether it is a complex and changeable business process or a special audit scenario, it can realize the flexible adjustment of the audit cycle and fully meet the audit needs of enterprises in different situations.

[0058] This method adopts offline risk quantitative assessment and cleverly uses big data technology to push and share the result list. This method effectively reduces the risk of online data being attacked and provides a solid guarantee for the security of enterprise data. When establishing an offline data platform, it pays great attention to data security protection, and builds a comprehensive data security line from the technical level to the management level to ensure the safety of enterprise sensitive data and avoid inestimable losses to enterprises due to problems such as data leakage. At the same time, professional auditors carefully summarize the abnormal data analysis method through a large number of audit cases, and construct a risk model covering multiple key aspects. Among them, the positioning of risk control points is like Accurate detectors are installed in the complex operation network of the enterprise, which can accurately point out the key links where potential risks may occur. Accurate risk description is like observing risks with a high-power microscope, clearly and in detail describing the specific characteristics of various risks, so that the form and characteristics of risks can be seen at a glance. Rigorous audit procedures lay a standardized and orderly track for audit work, ensuring that the audit work can proceed in an orderly manner. Detailed audit methods provide specific and practical guidance for actual operations, just like equipping auditors with sophisticated tools to help them carry out their work efficiently. The result verification procedure is like a solid safety lock, strictly ensuring the accuracy of the results.

[0059] In addition, it integrates a wealth of reference basic data, including internal business data, external related data and historical audit data. The internal business data of the enterprise covers financial data, business process data and personnel management data, reflecting the operation status of the enterprise from different dimensions. Financial data is like an accurate barometer of the financial status of the enterprise, which can greatly improve the accuracy of assessing the financial status, operating results and financial risk assessment of the enterprise. Business process data deeply analyzes the specific process information of each business link of the enterprise. Through in-depth analysis of data such as procurement and payables process, production cycle process, sales and receivables process, inventory and warehousing process, marketing management process, etc., it can accurately discover potential risk points and weak links in the process.

[0060] The industry data in the external relevant data of the enterprise include data on the market size, market growth rate, competitive landscape, industry trends, industry policies, etc. of the industry. By comparing the performance of the enterprise with that of the industry as a whole, it is like placing the enterprise on the grand stage of the industry for examination. It can evaluate the position and competitiveness of the enterprise in the industry, as well as the risks that changes in the industry environment may bring to the enterprise. Market data specifically refers to market supply and demand, product price fluctuations, changes in consumer demand, market share and other data. It can truthfully reflect the performance of the enterprise's products or services in the market and the market's recognition of the enterprise, help the enterprise to keenly identify market risks, and further improve the accuracy of risk assessment. Historical audit data is like a precious treasure trove of experience, which can provide extremely valuable reference and reference for current risk assessment, help discover long-term or recurring risk problems that may exist in the enterprise, and evaluate the effectiveness of the enterprise's risk management measures and clarify the direction of improvement. These rich data make the risk model more scientific and practical, laying a solid foundation for subsequent audit work; transform the risk model script program and fully introduce offline big data analysis technology to the audit The audit process and methods are used to process efficiently. This processing method is like installing a powerful engine for the audit work, which can achieve fast data calculation and accurate analysis results. Big data collects basic data. By formulating strict data collection standards and specifications, it ensures that the collected data is accurate, complete and reliable, and provides high-quality data support for subsequent analysis. Offline data processing uses big data technology to automatically identify and process abnormal data, improve data quality and availability, just like conducting in-depth processing and precise pruning for the data, organically integrating data from different sources to form a unified data whole, and providing high-quality intermediate data for data analysis of risk models. Offline modular program script analysis realizes multi-angle and multi-level analysis, just like observing a mountain from different perspectives, which can more comprehensively understand its shape and characteristics. This analysis method improves the accuracy and reliability of the analysis, ensures a comprehensive grasp of the risks, and continuously optimizes the performance and functions of the program script, improves the analysis efficiency and quality, just like injecting continuous power into the program script, so that it can better adapt to the audit needs of the enterprise and provide a more powerful tool for risk assessment.

[0061] The application of risk quantitative assessment method determines the dimensions and reasonable weights of risk assessment according to the scientific indicator system of risk model, which is like building a precise coordinate system for risk assessment. It uses advanced data analysis algorithms and models to quantify the data and accurately determine the size and impact of the risk, just like using precision instruments to measure the scale of risk. At the same time, combined with the actual situation of the enterprise, a corresponding risk early warning mechanism is formulated. Through real-time monitoring of data changes, potential risks can be discovered and warned in a timely manner, providing strong support for the enterprise's risk management, just like installing a sensitive risk alarm for the enterprise, generating a list of abnormal risk results that clearly lists potential risk points, risk levels and related audit recommendations, and providing auditors with clear work directions and priorities, just like providing auditors with a clear navigation map, classifying and organizing the list to facilitate auditors to review and analyze, thereby improving the efficiency and Accuracy is like organizing messy items in order for easy search and use. The result list export and management mechanism ensures the accuracy and timeliness of the results, providing a reliable basis for corporate decision-making, just like providing an accurate intelligence for corporate decision-making. Through an effective management mechanism, the result list is always kept up to date, providing continuous support for audit work, just like providing a continuous source of power for audit work. Big data sharing realizes offline data sharing and collaborative work among various departments within the enterprise, improves the enterprise's risk management level and efficiency, and promotes the overall development of the enterprise, just like creating a closely cooperating team for the enterprise. Through the offline data platform, it breaks down the data barriers between departments and realizes the circulation and sharing of information. At the same time, it is not connected to the external network, and the overall offline state is more secure, providing more comprehensive support for the enterprise's risk management, just like weaving a tight protective net for the enterprise's risk management.

Claims

1. A method for quantitative assessment of offline risk of enterprise audit based on big data, characterized in that: The steps include: Step 1: Construct a risk model. Professional auditors analyze a large number of audit cases through offline software, summarize the ideas and methods of abnormal data analysis, and construct a risk model covering risk control point positioning, risk description, audit procedures, audit methods, result verification procedures, reference basic data and risk-prone information. The model is enriched and improved with the help of offline big data to accurately reflect potential risks. Step 2: Script program conversion: Based on the extracted offline data risk model logical structure, the script program is converted using programming technology, and offline big data analysis technology is introduced to improve work efficiency and accuracy, making the audit work more standardized and automated; Step 3: Establish an offline database. Use a lightweight database to establish an offline database. Combine business process, personnel management and other data to formulate standard specifications to ensure that the data is accurate, complete and reliable and import it into the offline database. Step 4: Formulate data collection rules, clarify the scope, frequency, format and other requirements of data collection, determine the data type and source according to the business and risk status of the enterprise, establish a data quality monitoring mechanism, ensure the accuracy and reliability of the data, and provide a high-quality foundation for the audit; Step 5: Offline data processing: clean, trim, and integrate the imported data according to the risk model logic, use big data technology to automatically identify and process abnormal data, improve data quality and availability, and provide intermediate data for analysis; Step 6: Apply the risk quantification assessment method. Use the risk quantification assessment method based on big data, determine the dimensions and weights according to the risk model indicator system, use the algorithm model to quantify the risk, and formulate an early warning mechanism based on the actual situation. Step 7: Offline modular program script analysis: Use offline modular program scripts to conduct multi-angle and multi-level analysis and verification of audit data, flexibly combine and call modules, improve analysis accuracy and reliability, and continuously optimize script performance and functions; Step 8: Generate a list of abnormal risk results. Generate a list based on the risk quantitative assessment results, list the risk points, levels and audit recommendations, and organize them into categories to facilitate audit personnel to review and analyze, thereby improving work efficiency and accuracy; Step 9: Export the result list. Export the result list from the database into a format that is easy to consult. Establish a management mechanism to regularly update and maintain it to ensure accuracy and timeliness, and provide a basis for corporate decision-making. Step 10: Big data sharing, using offline databases to push and share result lists, establishing offline data platforms, promoting inter-departmental collaboration, and improving enterprise risk management levels and efficiency.

2. According to the big data-based enterprise audit offline risk quantitative assessment method of claim 1, it is characterized by: The basic data in step one specifically include internal business data of the enterprise, external relevant data of the enterprise and historical audit data, wherein the historical audit data refers to the relevant information and data of the internal audit, comprehensive audit and special audit of the enterprise in previous years. The historical audit data can provide reference and reference for the current risk assessment, help discover the long-term or recurring risk problems that may exist in the enterprise, and evaluate the effectiveness of the enterprise's risk management measures and improvement directions.

3. According to the big data-based enterprise audit offline risk quantitative assessment method of claim 2, it is characterized by: The internal enterprise data specifically refers to financial data, business process data and personnel management data. The financial data specifically refers to data on the enterprise's assets, liabilities, income, costs, profits, etc. This data can improve the accuracy of assessing the enterprise's financial status, operating results and financial risk assessment.

4. According to the big data-based enterprise audit offline risk quantitative assessment method of claim 3, it is characterized by: The business process data specifically refers to the specific process information of each business link of the enterprise, namely, data such as procurement and accounts payable process, production cycle process, sales and accounts receivable process, inventory and warehousing process, marketing management process, etc. By analyzing these business process data, we can have an in-depth understanding of the operation of the enterprise's business and discover potential risk points and weak links in the process.

5. According to the big data-based enterprise audit offline risk quantitative assessment method of claim 1, it is characterized by: The external relevant data of the enterprise specifically include industry data, market data and macroeconomic data, among which industry data include data on the market size, market growth rate, competitive landscape, industry trends, industry policies, etc. of the industry. By comparing the performance of the enterprise with that of the industry as a whole, it is possible to assess the position and competitiveness of the enterprise in the industry, as well as the risks that changes in the industry environment may bring to the enterprise.

6. According to the big data-based enterprise audit offline risk quantitative assessment method of claim 5, it is characterized in that: The market data specifically refers to data such as market supply and demand, product price fluctuations, changes in consumer demand, and market share. Market data can reflect the performance of a company's products or services in the market and the market's recognition of the company, helping the company identify market risks and further improve the accuracy of risk assessment.

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