Service compliance management evidence-based method and system for finance and tax service system

Abstract

The invention discloses a business compliance management evidence-based method and a business compliance management evidence-based system for a finance and tax business system, relates to activities such as original document verification, reimbursement, auditing, voucher generation, accounting and tax declaration in finance and tax businesses, and aims to ensure the authenticity, accuracy and compliance of financial activities so as to trace and verify the rationality and reliability of financial data. The integrity and traceability of financial information are ensured, and any potential financial risk and fraudulent behaviors are prevented and found, and links such as initiation, execution and examination and approval of financial activities are prevented and found. And the initiator of the financial activity needs to submit related vouchers and data, so that the accuracy and integrity of financial data are ensured. Responsibilities and permissions should be defined in the approval link, and compliance and normalization of financial activities are ensured. According to the method, the whole-course tracking and recording of the business operation are realized by utilizing the characteristics of distribution, tampering resistance and the like of the block chain, and the authenticity and integrity of the business trace are ensured.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to an evidence-based method and system for business compliance management of a financial and taxation business system. Background Art

[0002] The corporate finance and taxation system plays an important role in the business activities of enterprises. The role of finance and taxation management in corporate competition is becoming more and more significant. The activities involving original document inspection, reimbursement, audit, voucher generation, bookkeeping, tax declaration, etc. in finance and taxation business are frequent. How to ensure the authenticity, accuracy and compliance of financial activities, and trace and verify the rationality and reliability of financial data are becoming increasingly important.

[0003] In corporate financial management, the modification of documents and vouchers occurs frequently. However, the actual situation of these modifications, especially when multiple people use the database, often cannot accurately determine the modification records and the person who made the modification, which will cause cumbersome disputes and inquiries in the future. By providing the retention of data modification traces, users can automatically generate data modification traces and associate the modification personnel when modifying data, ensuring the security and reliability of data and greatly reducing the query work of managers. Therefore, a method for managing business compliance process traces in the financial and tax business system is needed to ensure the authenticity and integrity of business traces. Summary of the invention

[0004] The purpose of the present invention is to address the deficiencies of the prior art and to propose an evidence-based method and system for business compliance management of financial and tax business systems.

[0005] The object of the present invention is to achieve the following technical solution: an evidence-based method for business compliance management of a financial and tax business system, the method comprising:

[0006] S1. Establish a private chain or franchise chain for the enterprise and create accounts for participants in the financial and tax business;

[0007] S2. Intercept the data flow of the main business links of the financial and taxation business system through dynamic proxy and collect relevant data;

[0008] S3, pre-process the collected data, extract the user access IP, user information, department, operation time, operation duration, operation content, idle time, access method, authority, and enterprise type after obtaining standardized data, construct a multi-dimensional feature vector and normalize it, set the weight of each dimensional data to calculate the data vector, and store the obtained data vector in correspondence with the trace data;

[0009] S4. Calculate the reasonable probability range based on the enterprise type, user department information, and authority weight information to obtain the reasonable probability of each data vector, and obtain the corresponding reasonable vector based on the user, department, enterprise type, and authority; calculate the distance between the data vector and the reasonable vector, and issue a warning if it is not within the normal range. If it is within the normal range, submit the relevant trace information of the data vector to the blockchain through the smart contract.

[0010] Furthermore, in S1, after the account is created, a corresponding relationship {user id, block account} needs to be established in the database to perform system identity confirmation.

[0011] Furthermore, the main business links include business order application, review, original document verification, flow of invoices, approval, voucher generation, accounting and declaration. After the relevant data is collected, it is sent to the message queue in an asynchronous manner. The message-driven method is completely decoupled from the financial and tax business system.

[0012] Furthermore, the preprocessing of the collected data includes further cleaning, integration and standardization of the original data and marking a unique serial number for each piece of data.

[0013] Furthermore, the calculation of the data vector includes: multidimensional feature vector A i {A i1 ,A i2 ,A i3 ......A i11} after normalization, with the empirical weights W{W1,W2,W3...W 11} as coefficient, evaluate Dn = A i W, stores Dn and trace data in correspondence {serial number, Dn}.

[0014] Furthermore, the reasonable probability range calculation includes Among them, Di represents the obtained data measurement value, and I means the enterprise industry experience value.

[0015] Furthermore, the calculation process of the reasonable vector includes:

[0016] According to the historical data of the enterprise, it is divided into K clusters C{C1,C2,C3...C k}, its corresponding relationship is {{user number, department number, enterprise type code, authority number}, Ck}, and the reasonable vector Ck is obtained through {user number, department number, enterprise type code, authority number}.

[0017] Furthermore, the distance calculation includes: performing distance calculation T=||D between the data measurement value Di and the reasonable vector Ck i -Ck || 2 After the calculated value is normalized, if T is less than the reasonable probability, it is judged to be effective.

[0018] Furthermore, the processing process of the smart contract includes: first, judging whether the operator matches the submitted account through {user id, block account}, if it matches, authorization is performed to process subsequent business, otherwise an error message is returned to the client, and after authorization is passed, the data generated by the business operation is calculated by sha256 summary, and the data is packaged and encrypted with the enterprise public key, and the business code is used as the key value of the map. The encrypted data and summary are written into the blockchain in the form of json array [{ciphertext:, resume:, id:}] as value.

[0019] According to another aspect of the specification, there is also provided a business process trace management system for a financial and taxation business system used in the method, the system comprising: an information collection module, a data preprocessing and cleaning module, a data analysis module, an alarm module, a blockchain node, a query and tracing module, and a business database;

[0020] The information collection module is used to intercept data flows of the main business links of the finance and taxation business system through dynamic proxy and collect relevant data;

[0021] The data preprocessing and cleaning module is used to preprocess the collected data, extract the user access IP, user information, department, operation time, operation duration, operation content, idle time, access method, authority, and enterprise type after obtaining standardized data, construct a multi-dimensional feature vector and normalize it, set the weight of each dimensional data to calculate the data vector, and store the obtained data vector in correspondence with the trace data;

[0022] The data analysis module is used to calculate the reasonable probability range according to the enterprise type, user department information, and authority weight information, obtain the reasonable probability of each data vector, and obtain the corresponding reasonable vector according to the user, department, enterprise type, and authority; perform distance calculation on the data vector and the reasonable vector.

[0023] The alarm module issues a warning when the data is not within the normal range;

[0024] The blockchain node is used to store relevant trace information;

[0025] The query and tracing module is used to query and trace business traces. It obtains relevant data stored in the block through the business code, checks any operation in the array, and uses the sha256 of the existing business data of the business system to compare the summary with the summary on the block. If there is a difference, it indicates that there is tampering. At the same time, the entire process of each business operation can be traced, and the specific business data items can be viewed after the block data is decrypted by the enterprise private key;

[0026] The business database is used to provide data for the financial and taxation business system.

[0027] Beneficial effects of the present invention: Without affecting the existing business system, the present invention uses technologies such as dynamic proxy to collect the traces of activities such as original document verification, accounting, auditing, voucher generation, accounting, tax declaration, etc. in financial and tax business into the data processing module for data cleaning. After standardization, the trace data is analyzed for compliance and credibility by support vector machine machine learning. After the analysis, credible and reasonable data is obtained, and the relevant credible trace data is submitted to the block, which is automatically calculated and stored through smart contracts to ensure the authenticity, accuracy and compliance of financial activities, trace and verify the rationality and reliability of financial data, ensure the integrity and traceability of financial information, prevent data from being tampered with, and make digital identity information traceable, so as to achieve full tracking and recording of business operations and ensure the authenticity and integrity of business traces. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A flow chart of an evidence-based method for business compliance management of a financial and tax business system provided by an embodiment of the present invention;

[0029] Figure 2 A framework diagram of an evidence-based system for business compliance management of a financial and taxation business system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The specific implementation modes of the present invention are further described in detail below with reference to the accompanying drawings.

[0031] like Figure 1 As shown in the figure, an evidence-based method for business compliance management of financial and tax business systems is provided. Without intruding the system, an algorithm based on support vector machines is applied to verify the authenticity and compliance of the traces. The distributed and tamper-proof characteristics of blockchain are used to achieve full tracking and recording of business operations to ensure the authenticity and integrity of business traces. First, a private chain or franchise chain is established for the enterprise and blockchain users are created for comparison with system users. Then, the system data is collected non-invasively through dynamic agents for cleaning, and feature vectors are constructed after sorting and standardization. After a series of analysis and calculation, it is determined whether the traces are authentic and valid. If valid, the complete traces are finally written into the block to ensure the authenticity and integrity of the business traces.

[0032] The specific implementation steps are as follows:

[0033] S1. Establish a private chain or franchise chain for the enterprise, create accounts for important participants in its system applications, and establish a corresponding relationship {user id, block account} in the database to confirm the system identity.

[0034] S2. Data collection. AOP or Javassist technology is used for dynamic proxying based on the original system application without changing the existing business code. The data flow of a series of business links such as business order application and review, original document verification, flow and approval of invoices, voucher generation, accounting, and declaration are intercepted, and relevant data is collected. The collected data is sent to the message queue. The queue backend processes the information in a message-driven manner, which completely decouples the business system from the trace management system.

[0035] S3. Data preprocessing: preprocess the data obtained by the acquisition module, further clean the raw data, integrate and standardize it, and mark each data with a unique serial number. After obtaining the standardized data, extract the user access IP, user information, department, operation time, operation duration, operation content, idle time, access method, authority, and enterprise type to build a multi-dimensional feature vector A. i {A i1 ,A i2 ,A i3 ......A i11}, and normalized, and used the empirical weights W{W1,W2,W3...W 11} as coefficient, evaluate Dn = A i W, stores Dn and trace data in correspondence {serial number, Dn}.

[0036] S4. Data analysis: statistical analysis of the above pre-processed data vectors D1, D2, D3...D n , and calculate the reasonable probability range based on the weight information of enterprise type, user department information, and authority, based on Bayesian reasoning, Di represents the obtained data measurement value, and I represents the enterprise industry experience value Get its reasonable probability, and divide it into K clusters C{C1,C2,C3...C k}, its corresponding relationship is {{user number, department number, enterprise type code, authority number}, Ck}, Ck is obtained through {user number, department number, enterprise type code, authority number}, and the distance between Di and Ck is calculated by T = || D i -C k || 2After the calculated value is normalized, if T is within the reasonable probability range calculated above, it is judged to be valid. If it is within the normal range, the data generated by the business operation, including but not limited to: business code, business number, operator, timestamp, business data and other information, is submitted to the blockchain through a smart contract as the business initiator using his blockchain account. (Take the operation content as an example, first obtain the historical operation content sample, segment its content and then perform vector processing, and use the K-means method to select K vectors as the initial clustering center C{C1,C2,C3......C k}, vectorize the collected operation content to get Di, and calculate its distance to the cluster center T = || D i -C k || 2 , other elements are handled similarly).

[0037] Smart contract business processing first determines whether the operator matches the submitted account through {user id, block account}. If it matches, authorization is performed to process subsequent business, otherwise an error message is returned to the client. After authorization is passed, the data generated by the business operation, including but not limited to the operator, operation content business data, operation time and other information, is calculated by sha256 summary, and the data is packaged and encrypted with the enterprise public key. The business code (the business code is the idempotent id) is used as the key value of the map. The encrypted data and summary are written to the blockchain in the form of json array [{ciphertext:, resume:, id:}] as the value. Once the new block is confirmed to be generated, it will be synchronized to each node.

[0038] Query and trace business traces. Through the business code, you can get the relevant data stored in the block, view any operation in the array, and use the sha256 summary of the existing business data of the business system to compare with the summary on the block. If there is a difference, it indicates tampering. At the same time, you can trace the entire process of each business operation. After decrypting the block data with the enterprise private key, you can view the specific business data items, including the operator, operation content, time-related business and other information.

[0039] like Figure 2 As shown, on the other hand, an embodiment of the present invention further provides a business compliance management evidence-based system for a financial and tax business system, the system comprising: an information collection module, a data preprocessing and cleaning module, a data analysis module, an alarm module, a blockchain node, a query and tracing module, and a business database;

[0040] The information collection module is used to intercept data flows of the main business links of the finance and taxation business system through dynamic proxy and collect relevant data;

[0041] The data preprocessing and cleaning module is used to preprocess the collected data, extract the user access IP, user information, department, operation time, operation duration, operation content, idle time, access method, authority, and enterprise type after obtaining standardized data, construct a multi-dimensional feature vector and normalize it, set the weight of each dimensional data to calculate the data vector, and store the obtained data vector in correspondence with the trace data.

[0042] The data analysis module is used to calculate the reasonable probability range according to the enterprise type, user department information, and authority weight information, obtain the reasonable probability of each data vector, and obtain the corresponding reasonable vector according to the user, department, enterprise type, and authority; perform distance calculation on the data vector and the reasonable vector.

[0043] The alarm module issues a warning when the data is not within the normal range;

[0044] The blockchain node is used to store relevant trace information;

[0045] The query and tracing module is used to query and trace business traces. It obtains relevant data stored in the block through the business code, checks any operation in the array, and uses the sha256 of the existing business data of the business system to compare the summary with the summary on the block. If there is a difference, it indicates that there is tampering. At the same time, the entire process of each business operation can be traced, and the specific business data items can be viewed after the block data is decrypted by the enterprise private key;

[0046] The business database is used to provide data for the financial and taxation business system.

[0047] The present invention utilizes technologies such as dynamic proxy to collect traces of activities such as original document verification, accounting, auditing, voucher generation, accounting, and tax declaration in financial and taxation business into the data processing module for data cleaning, and obtains credible and reasonable data after analysis. By utilizing blockchain technology, the relevant credible trace data is submitted to the block, and is automatically calculated and stored through smart contracts to ensure the authenticity, accuracy, and compliance of financial activities, so as to trace and verify the rationality and reliability of financial data, ensure the integrity and traceability of financial information, prevent data from being tampered with, and make digital identity information traceable, so as to achieve full tracking and recording of business operations and ensure the authenticity and integrity of business traces.

[0048] Those skilled in the art will readily appreciate other embodiments of the present application after considering the description and practicing the contents disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The description and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the claims.

[0049] It should be understood that the above general description and the detailed description below are only exemplary and explanatory and cannot limit the present application. The present application is not limited to the precise structure described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the present application is limited only by the attached claims.

Claims

1. An evidence-based method for business compliance management of financial and tax business systems, characterized in that: The method includes: S1. Establish a private chain or franchise chain for the enterprise and create accounts for participants in the financial and tax business; S2. Intercept the data flow of the main business links of the financial and taxation business system through dynamic proxy and collect relevant data; S3, pre-process the collected data, extract the user access IP, user information, department, operation time, operation duration, operation content, idle time, access method, authority, and enterprise type after obtaining standardized data, construct a multi-dimensional feature vector and normalize it, set the weight of each dimensional data to calculate the data vector, and store the obtained data vector in correspondence with the trace data; S4. Calculate the reasonable probability range based on the enterprise type, user department information, and authority weight information to obtain the reasonable probability of each data vector, and obtain the corresponding reasonable vector based on the user, department, enterprise type, and authority; calculate the distance between the data vector and the reasonable vector, and issue a warning if it is not within the normal range. If it is within the normal range, submit the relevant trace information of the data vector to the blockchain through the smart contract.

2. According to claim 1, a business compliance management evidence-based method for a financial and tax business system is characterized in that: In S1, after the account is created, a corresponding relationship {user id, block account} needs to be established in the database to confirm the system identity.

3. According to claim 1, the business compliance management evidence-based method for the financial and tax business system is characterized in that: The main business links include business order application, review, original document verification, flow of invoices, approval, voucher generation, accounting and declaration. After the relevant data is collected, it is sent to the message queue in an asynchronous manner. The message-driven method is completely decoupled from the financial and tax business system.

4. According to claim 1, the business compliance management evidence-based method for the financial and tax business system is characterized in that: The preprocessing of the collected data includes further cleaning, integration and standardization of the original data and marking a unique serial number for each piece of data.

5. According to claim 4, a business compliance management evidence-based method for a financial and tax business system is characterized in that: The calculation of the data vector includes: multidimensional feature vector A i {A i1 ,A i2 ,A i3 ......A i11 } after normalization, with the empirical weights W{W1,W2,W3...W 11 } as coefficient, evaluate Dn = A i W, stores Dn and trace data in correspondence {serial number, Dn}.

6. According to claim 5, a business compliance management evidence-based method for a financial and tax business system is characterized in that: The reasonable probability range calculation includes Among them, Di represents the obtained data measurement value, and I means the enterprise industry experience value.

7. According to claim 1, a business compliance management evidence-based method for a financial and tax business system is characterized in that: The calculation process of the reasonable vector includes: According to the historical data of the enterprise, it is divided into K clusters C{C1,C2,C3...C k }, its corresponding relationship is {{user number, department number, enterprise type code, authority number}, Ck}, and the reasonable vector Ck is obtained through {user number, department number, enterprise type code, authority number}.

8. According to claim 1, the business compliance management evidence-based method for the financial and tax business system is characterized in that: The distance calculation includes: performing distance calculation T=||D between the data measurement value Di and the reasonable vector Ck i -C k || 2 After the calculated value is normalized, if T is less than the reasonable probability, it is judged to be effective.

9. The evidence-based method for business compliance management of a financial and tax business system according to claim 1 is characterized in that: The processing process of the smart contract includes: first, judging whether the operator matches the submitted account through {user id, block account}, if it matches, authorization is performed to process subsequent business, otherwise an error message is returned to the client, and after authorization is passed, the data generated by the business operation is calculated by sha256 summary, and the data is packaged and encrypted with the enterprise public key, and the business code is used as the key value of the map. The encrypted data and summary are written into the blockchain in the form of json array [{ciphertext:, resume:, id:}] as value.

10. A business process trace management system for a financial and taxation business system for implementing the method according to any one of claims 1 to 9, characterized in that: The system includes: information collection module, data preprocessing and cleaning module, data analysis module, alarm module, blockchain node, query and tracing module and business database; The information collection module is used to intercept data flows of the main business links of the finance and taxation business system through dynamic proxy and collect relevant data; The data preprocessing and cleaning module is used to preprocess the collected data, extract the user access IP, user information, department, operation time, operation duration, operation content, idle time, access method, authority, and enterprise type after obtaining standardized data, construct a multi-dimensional feature vector and normalize it, set the weight of each dimensional data to calculate the data vector, and store the obtained data vector in correspondence with the trace data; The data analysis module is used to calculate the reasonable probability range according to the enterprise type, user department information, and authority weight information, obtain the reasonable probability of each data vector, and obtain the corresponding reasonable vector according to the user, department, enterprise type, and authority; perform distance calculation on the data vector and the reasonable vector. The alarm module issues a warning when the data is not within the normal range; The blockchain node is used to store relevant trace information; The query and tracing module is used to query and trace business traces. It obtains relevant data stored in the block through the business code, checks any operation in the array, and uses the sha256 of the existing business data of the business system to compare the summary with the summary on the block. If there is a difference, it indicates that there is tampering. At the same time, it can trace the entire process of each business operation, and decrypt the block data with the enterprise private key to view the specific business data items; The business database is used to provide data for the financial and taxation business system.

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