Financial data auditing method and system

Abstract

The financial data auditing method comprises the following steps: S1, acquiring multi-source financial data by utilizing a data acquisition mode and preprocessing the data; s2, constructing a dynamic rule engine to dynamically load and update an auditing rule; s3, verifying the data item by item based on a rule engine, and performing real-time anomaly detection; and S4, visualization of a financial data auditing result is carried out, feedback optimization is carried out on the auditing result, and the financial auditing system comprises a multi-source data acquisition module, a data preprocessing module, a dynamic rule engine module, an anomaly detection module and a visualization and feedback module. A data cleaning algorithm is adopted to solve the auditing problem of unstructured data in the data; according to the method, the dynamic rule engine is constructed to dynamically load and update the auditing rule, the problem of immobilization of the current auditing rule is solved, the weight of the rule adjusted in the auditing rule base is updated based on the user feedback data, and closed-loop optimization is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of financial data auditing, and particularly to a financial data auditing method and system. Background Art

[0002] Financial data auditing refers to comprehensively and systematically checking and verifying the financial data of an enterprise to ensure its accuracy, integrity, and compliance.

[0003] Currently, financial data auditing mainly relies on manual checking or fixed rule scripts, and there are the following problems:

[0004] Problem 1. Difficulty in integrating multi-source heterogeneous data: The sources of financial data are diverse (such as bank statements, ERP systems, electronic invoices, etc.), and the data formats are not unified. Manual data cleaning and alignment are required, resulting in low efficiency and easy errors in financial data auditing;

[0005] Problem 2. Lag in rule updates: Fixed auditing rules cannot adapt to dynamic business requirements (such as tax rate adjustments, new bill types), and the code needs to be frequently modified, resulting in high maintenance costs;

[0006] Problem 3. Weak anomaly detection ability: Relying on manual experience to identify anomalies (such as duplicate reimbursements, false transactions), the missed detection rate is high, and large-scale data streams cannot be processed in real time;

[0007] Problem 4. Lack of closed-loop optimization: The audit results are not fed back to the rule library, and the entire system cannot self-iterate and optimize.

[0008] In view of the above problems, a financial data auditing method and system are proposed. Summary of the Invention

[0009] According to the above technical problems, the present invention provides a financial data auditing method and system to solve the problems of multi-source data integration, dynamic rule adaptation, real-time anomaly detection, and closed-loop optimization in current financial data auditing.

[0010] To achieve the above object, the technical solution of the present invention is as follows:

[0011] A financial data auditing method, and its specific steps are as follows:

[0012] S1. Use a data collection method to obtain multi-source financial data and preprocess the data;

[0013] S2. Build a dynamic rule engine to achieve dynamic loading and updating of audit rules;

[0014] S3. Based on the rule engine, verify the data item by item for real-time anomaly detection;

[0015] S4. Visualization of the financial data audit results and feedback optimization of the audit results.

[0016] The data collection method in step S1 is any one of the following: API interface, file upload, or direct database connection.

[0017] The specific content of preprocessing the data in step S1 is as follows: Use data cleaning algorithms to perform field extraction and format standardization on unstructured data to generate a unified data table.

[0018] The specific content of building a dynamic rule engine to achieve dynamic loading and updating of audit rules in step S2 is as follows:

[0019] First, configure a basic audit rule library, and support users to customize audit rules, and the customized audit rules are also incorporated into the audit rule library;

[0020] Dynamically load the matching audit rules into memory through rule priority configuration and conflict detection algorithms, and respond and update the audit rules in real time.

[0021] The specific content of performing real-time anomaly detection by verifying data item by item based on the rule engine in step S3 is as follows:

[0022] Verify the preprocessed data item by item according to the audit rules dynamically loaded and updated, mark the displayed anomalies, and use the Isolation Forest algorithm to perform clustering analysis on the implicit anomalies in the data to obtain an anomaly probability score.

[0023] The specific content of visualizing the financial data audit results and feedback optimization of the audit results in step S4 is as follows:

[0024] Output the audit results, generate an interactive audit report for the audit results, highlight the abnormal items in the audit report, and perform manual review and result marking on the abnormal items in the report.

[0025] Integrate the manual feedback data during the audit process and input it into the dynamic rule engine for evaluation, so that inefficient rules are deactivated and recommended rule optimization solutions are provided.

[0026] Furthermore, the data cleaning algorithms include regular expressions and missing value filling. Among them, regular expressions can match, search, replace, or operate on data in a string by defining specific patterns, and can efficiently extract useful information from a large amount of text data and ensure that the data meets the expected standards and specifications. Missing value filling uses statistics such as mean, median, and mode to replace missing values, or more complex methods such as interpolation and hot deck imputation can maintain the integrity of the data set.

[0027] A financial data audit system, characterized by comprising a multi-source data acquisition module, a data preprocessing module, a dynamic rule engine module, an anomaly detection module, and a visualization and feedback module.

[0028] The multi-source data acquisition module is used to obtain multi-source financial data.

[0029] The data preprocessing module is used to extract fields and standardize the format of the financial data to generate a unified data table.

[0030] The dynamic rule engine module is used for the dynamic management of the audit rule library.

[0031] The anomaly detection module is used to perform item-by-item verification, anomaly marking, and anomaly analysis on the preprocessed data.

[0032] The visualization and feedback module is used for visualizing the output of the financial data audit results and recording the manual feedback during the audit process.

[0033] Advantages of the present invention:

[0034] In the financial audit method designed by the present invention, multi-source data acquisition is performed on financial data, and a data cleaning algorithm is used to extract fields and standardize the format of unstructured data to generate a unified data table, solving the problem of auditing unstructured data in the data.

[0035] The present invention constructs a dynamic rule engine to achieve dynamic loading and updating of audit rules, solving the problem of fixed audit rules at present. And after the audit results are output, the rule weights in the audit rule library will be adjusted and updated based on user feedback data, realizing the closed-loop optimization of the financial data audit system.

[0036] The financial data audit system designed by the present invention is used to analyze and audit financial data, solving the problem of relying only on manual audits at present and improving the efficiency of financial data audits. Description of the Drawings

[0037] Figure 1 It is a schematic flow diagram of the financial data audit method of the present invention;

[0038] Figure 2 It is a structural block diagram of the financial data audit system of the present invention. Detailed Embodiments

[0039] Next, the technical solutions of the present invention will be clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0040] Example 1

[0041] A financial data audit method and system, and the specific steps are as follows:

[0042] S1. Use the data collection method to obtain multi-source financial data and preprocess the data;

[0043] The API interface, file upload or database direct connection method can be used to obtain multi-source financial data, such as collecting data such as transaction data, electronic invoices, and contracts.

[0044] Use the data cleaning algorithm to extract fields and standardize the format of unstructured data to generate a unified data table. The data cleaning algorithm includes regular expressions and missing value filling.

[0045] Data cleaning is performed using regular expressions. Regular expressions can match, search, replace, or manipulate data in a string by defining specific patterns, and can efficiently extract useful information from a large amount of text data and ensure that the data meets the expected standards and specifications.

[0046] Data cleaning is performed using missing value filling. Missing value filling uses statistics such as mean, median, and mode to replace missing values, or more complex methods such as interpolation and hot deck imputation can maintain the integrity of the data set.

[0047] Unstructured data refers to the pictures of financial data records collected, such as invoice pictures.

[0048] S2. Build a dynamic rule engine to achieve dynamic loading and updating of audit rules;

[0049] First, configure the basic audit rule library, which contains amount ratio, date logic verification, etc.

[0050] Support users to customize audit rules, and the customized audit rules are also incorporated into the audit rule library;

[0051] Dynamically load the matching audit rules into memory through rule priority configuration and conflict detection algorithms, and respond and update the audit rules in real time.

[0052] S3. Based on the rule engine, verify the data item by item to perform real-time anomaly detection;

[0053] Verify the preprocessed data item by item according to the audit rules dynamically loaded and updated, mark the displayed anomalies, and use the isolation forest algorithm to perform clustering analysis on the implicit anomalies in the data to obtain an anomaly probability score.

[0054] S4. Visualize the financial data audit results and provide feedback and optimization for the audit results.

[0055] Output the audit results, generate an interactive audit report from the audit results, highlight the abnormal items in the audit report, and conduct manual review and result marking for the abnormal items in the report.

[0056] Integrate the manual feedback data during the audit process and input it into the dynamic rule engine for evaluation, so as to deactivate inefficient rules and recommend rule optimization solutions.

[0057] A financial data audit system, characterized by including a multi-source data acquisition module, a data preprocessing module, a dynamic rule engine module, an anomaly detection module, and a visualization and feedback module.

[0058] The multi-source data acquisition module is used to obtain multi-source financial data.

[0059] The data preprocessing module is used to extract fields and standardize the format of the financial data to generate a unified data table.

[0060] The dynamic rule engine module is used for the dynamic management of the audit rule library.

[0061] The anomaly detection module is used to check each piece of preprocessed data item by item, mark anomalies, and analyze anomalies.

[0062] The visualization and feedback module is used for visualizing the output of the financial data audit results and recording the manual feedback during the audit process.

[0063] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A financial data audit method, the specific steps of which are as follows: S1. Use data collection methods to obtain multi-source financial data and pre-process the data; S2. Build a dynamic rule engine to dynamically load and update audit rules; S3. Verify data one by one based on the rule engine and perform real-time anomaly detection; S4. Visualization of financial data audit results and feedback optimization of audit results.

2. A financial data audit method and system according to claim 1, characterized in that The data collection method in step S1 is any one of the following: API interface, file upload or database direct connection.

3. A financial data audit method and system according to claim 1, characterized in that The specific content of the data preprocessing in step S1 is as follows: using a data cleaning algorithm to extract fields and standardize the format of unstructured data to generate a unified data table.

4. A financial data audit method and system according to claim 1, characterized in that The specific contents of constructing a dynamic rule engine in step S2 to dynamically load and update audit rules are as follows: First, configure the basic audit rule library and support user-defined audit rules, which are also included in the audit rule library; Dynamically load matching audit rules into memory through rule priority configuration and conflict detection algorithm, and respond and update audit rules in real time.

5. A financial data audit method and system according to claim 1, characterized in that The specific contents of verifying data one by one based on the rule engine and performing real-time anomaly detection in step S3 are as follows: The pre-processed data is checked item by item according to the audit rules of dynamic loading and updating, the displayed anomalies are marked, and the implicit anomalies in the data are clustered using the isolation forest algorithm to obtain the anomaly probability score.

6. A financial data audit method and system according to claim 1, characterized in that The specific contents of the visualization of the financial data audit results and the feedback optimization of the audit results in step S4 are as follows: Output the audit results, generate an interactive audit report based on the audit results, highlight the abnormal items in the audit report, and manually review and mark the abnormal items in the report. The manual feedback data during the audit process is integrated and input into the dynamic rule engine for evaluation, so that inefficient rules can be disabled and rule optimization solutions can be recommended.

7. A financial data audit method and system according to claim 3, characterized in that The data cleaning algorithm includes regular expressions and missing value filling.

8. A financial data auditing system for implementing the financial data auditing method according to any one of claims 1 to 7, characterized in that It includes multi-source data acquisition module, data preprocessing module, dynamic rule engine module, anomaly detection module, and visualization and feedback module. The multi-source data acquisition module is used to obtain multi-source financial data. The data preprocessing module is used to extract fields and standardize the format of financial data to generate a unified data table. The dynamic rule engine module is used for dynamic management of the audit rule base. The anomaly detection module is used to check, mark and analyze the preprocessed data one by one. The visualization and feedback module is used to visualize the output of the financial data audit structure and record manual feedback during the audit process.

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