A business compliance management evidence-based method and system for a finance and tax business system

Abstract

The application discloses a kind of business compliance management evidence-based methods and system for financial and tax business system, it is related to the original single certificate of financial and tax business, report, audit, voucher generation, accounting, tax declaration and other activities, to ensure the authenticity, accuracy and compliance of financial activities, to trace and verify the reasonableness and reliability of financial data, guarantee the integrity and traceability of financial information, and prevent and find any potential financial risks and fraudulent behavior, the initiation, execution and approval of financial activities and other links.The initiator of financial activities should submit relevant vouchers and materials to ensure the accuracy and integrity of financial data.The approval link should be clear about responsibilities and authorities to ensure the compliance and standardization of financial activities.The method realizes the whole tracking and recording of business operation by using the distributed, tamper-proof and other characteristics of blockchain, ensures the authenticity and integrity of business traces.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to an evidence-based method and system for business compliance management of financial and tax business systems. Background Technology

[0002] Corporate finance and tax systems play a crucial role in business operations. The importance of finance and tax management in corporate competition is becoming increasingly significant. Activities involving finance and tax transactions, such as verification of original documents, expense reports, audits, voucher generation, bookkeeping, and tax filing, are frequent. Ensuring the authenticity, accuracy, and compliance of financial activities, and tracing and verifying the rationality and reliability of financial data, are becoming increasingly important.

[0003] In corporate financial management, modifications to documents and vouchers occur frequently. However, the true nature of these modifications, especially when multiple people use the database, is often difficult to accurately identify the modification records and the individuals responsible, leading to cumbersome disputes and inquiries later on. Providing a method for retaining data modification traces allows users to automatically generate modification records and associate them with the individuals making the changes, ensuring data security and reliability and significantly reducing the workload for administrators. Therefore, a method for managing the business compliance process traces in financial and tax business systems is needed to ensure the authenticity and completeness of business records. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing an evidence-based method and system for business compliance management in financial and tax business systems.

[0005] The objective of this invention is achieved through the following technical solution: an evidence-based method for business compliance management in a financial and tax business system, the method comprising:

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

[0007] S2. Use dynamic proxies to intercept data flow in the main business processes of the financial and tax business system and collect relevant data;

[0008] S3. Preprocess the collected data to obtain standardized data. Extract user access IP, user information, department, operation time, operation duration, operation content, idle time, access method, permissions, and enterprise type to construct a multi-dimensional feature vector and normalize it. Set the weight of each dimension of data to calculate the data vector. Store the obtained data vector and trace data accordingly.

[0009] S4. Calculate a reasonable probability range based on the enterprise type, user department information, and permission weights to obtain the reasonable probability of each data vector. Obtain the corresponding reasonable vector based on the user, department, enterprise type, and permissions. Calculate the distance between the data vector and the reasonable vector. If the distance is outside the normal range, issue a warning. If the distance is within the normal range, submit the relevant trace information of the data vector to the blockchain through a smart contract.

[0010] Furthermore, in step S1, after creating an account, a corresponding relationship {user id, block account} needs to be established in the database for system identity verification.

[0011] Furthermore, the main business processes include business order application, review, original document verification, invoice transfer, approval, voucher generation, accounting and declaration. After collecting relevant data, it is sent to the message queue asynchronously. The message-driven approach 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 raw data, and labeling each data entry with a unique serial number.

[0013] Furthermore, the calculation of the data vector includes: a multidimensional feature vector A i {A i1 A i2 A i3 ......A i11 After normalization, the empirical weights W{W1,W2,W3......W} are used. 11 As a coefficient, evaluate Dn = A i W stores Dn and trace data in a corresponding manner: {serial number, Dn}.

[0014] Furthermore, the calculation of the reasonable probability range includes Where Di represents the measured data value obtained, and I represents the industry experience value of the enterprise.

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

[0016] Based on historical enterprise data, the data is divided into K clusters C{C1, C2, C3, ..., C...} k The corresponding relationship is {{User ID, Department ID, Enterprise Type Code, Permission ID}, Ck}. A reasonable vector Ck can be obtained through {User ID, Department ID, Enterprise Type Code, Permission ID}.

[0017] Furthermore, the distance calculation includes: calculating the distance T = ||D| between the data measurement value Di and the reasonable vector Ck. i -Ck || 2 If the calculated value is normalized and T is less than the reasonable probability, it is considered valid.

[0018] Furthermore, the smart contract processing procedure includes: firstly, determining whether the operator matches the submitted account using {user id, blockchain account}. If a match is found, authorization is granted for subsequent business processing; otherwise, an error message is returned to the client. After authorization, the data generated by the business operation is subjected to SHA256 digest calculation, and the data is packaged and encrypted using the enterprise public key. The business code is used as the key value of the map, and the encrypted data and digest are written to the blockchain as a JSON array [{ciphertext:,resume:,id:}].

[0019] According to another aspect of the specification, a business process trace management system for a financial and tax business system for the method is also provided. The system includes: an information collection module, a data preprocessing and cleaning module, a data analysis module, an alarm module, a blockchain node, a query and traceability module, and a business database.

[0020] The information collection module is used to intercept data streams from the main business processes of the financial and tax business system through dynamic proxies and collect relevant data.

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

[0022] The data analysis module is used to calculate a reasonable probability range based on several weighted information, including enterprise type, user department information, and permissions, to obtain the reasonable probability for each data vector. It also generates a corresponding reasonable vector based on the user, department, enterprise type, and permissions. Finally, it calculates the distance between the data vectors and the reasonable vectors.

[0023] The alarm module issues a warning when the data is outside the normal range;

[0024] The blockchain nodes are used to store relevant trace information;

[0025] The query and traceability module is used for querying and tracing business traces. It obtains relevant data stored in the block through business code, views any operation in the array, compares the digest of the existing operation business data of the business system with the digest on the block, and if there is a difference, it indicates that there has been tampering. At the same time, it can trace the entire process of each business operation. After decrypting the data in the block with the enterprise private key, it can view the specific business data items.

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

[0027] The beneficial effects of this invention are as follows: Without affecting existing business systems, this invention utilizes technologies such as dynamic proxies to collect traces of activities in financial and tax business, such as original document verification, reporting, auditing, voucher generation, bookkeeping, and tax declaration, into a data processing module for data cleaning and standardization. After standardization, support vector machine learning is used to perform compliance and reliability analysis on the trace data. The analysis yields reliable and reasonable data, which is then submitted to the blockchain for automatic calculation and storage via smart contracts. This ensures the authenticity, accuracy, and compliance of financial activities, allowing for the traceability and verification of the rationality and reliability of financial data, guaranteeing the integrity and traceability of financial information, preventing data tampering, ensuring digital identity information traceability, and enabling full tracking and recording of business operations to guarantee the authenticity and integrity of business traces. Attached Figure Description

[0028] Figure 1 A flowchart of an evidence-based method for business compliance management in a financial and tax business system, provided as an embodiment of the present invention;

[0029] Figure 2 This invention provides a framework diagram for an evidence-based business compliance management system for financial and tax business systems. Detailed Implementation

[0030] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0031] like Figure 1 As shown, this paper presents an evidence-based method for business compliance management in financial and tax business systems. Without intruding on the system, it applies a support vector machine-based algorithm to verify the authenticity and compliance of traces. Leveraging the distributed and immutable characteristics of blockchain, it achieves full-process tracking and recording of business operations, ensuring the authenticity and integrity of business traces. First, a private or franchised blockchain is established for the enterprise, and blockchain users are compared with system users. Then, system data is collected non-intrusively through dynamic proxies, cleaned, standardized, and used to construct feature vectors. After a series of analyses and calculations, the authenticity and validity of the traces are determined. If valid, the complete trace is finally written into the blockchain, ensuring the authenticity and integrity of business traces.

[0032] The specific implementation steps are as follows:

[0033] S1. Establish a private or affiliated blockchain for the enterprise, create accounts for key participants in its system applications, and establish corresponding relationships {user ID, blockchain account} in the database to verify system identities.

[0034] S2. Data Acquisition: Without changing the existing business code in the original system application, dynamic proxying is performed using AOP or Javassist technology to intercept the data flow of the main business links: business order application and review, original document verification, invoice transfer and approval, voucher generation, accounting, and declaration. Relevant data is collected and sent to a message queue. The queue backend processes the information in a message-driven manner, thus completely decoupling the business system from the trace management system.

[0035] S3. Data Preprocessing: Preprocess the data acquired by the acquisition module, further cleaning, integrating, and standardizing the raw data, and labeling each data entry with a unique serial number. After obtaining standardized data, extract user access IP, user information, department, operation time, operation duration, operation content, idle time, access method, permissions, and enterprise type to construct a multi-dimensional feature vector A. i {A i1 A i2 A i3 ......A i11}, and perform normalization processing, and use empirical weights W{W1,W2,W3......W 11 As a coefficient, evaluate Dn = A i W stores Dn and trace data in a corresponding manner: {serial number, Dn}.

[0036] S4. Data Analysis: Perform statistical analysis on the preprocessed data vectors D1, D2, D3...D... n Based on factors such as enterprise type, user department information, and permissions, a reasonable probability range is calculated. This is based on Bayesian inference, where Di represents the obtained data measurement value, and I represents the enterprise's industry experience value. The reasonable probability was obtained, and the data was divided into K clusters C{C1,C2,C3......C1} based on the company's historical data. k The corresponding relationship is {{User ID, Department ID, Enterprise Type Code, Permission ID}, Ck}. Ck is obtained through {User ID, Department ID, Enterprise Type Code, Permission ID}. The distance between Di and Ck is calculated as T = ||D||. i -C k || 2After normalizing the calculated value, if T falls within the reasonable probability range calculated above, it is considered valid. If it falls within the normal range, the business initiator, using their blockchain account, submits the data generated by the business operation, including but not limited to: business code, business number, operator, timestamp, business data, etc., to the blockchain via a smart contract. (Taking operation content as an example, first, obtain historical operation content samples, segment the content into words, and then vectorize it. Use K-means to select K vectors as initial cluster centers C{C1, C2, C3...C... k The collected operation content is vectorized to obtain Di, and its distance T = ||D| to the cluster center is calculated. i -C k || 2 Other element items are handled in a similar manner.

[0037] The smart contract process first checks if the operator matches the submitted account using {user id, block account}. If they match, authorization is granted for subsequent processing; otherwise, an error message is returned to the client. Once authorized, the data generated by the business operation, including but not limited to the operator, operation content, business data, and operation time, is hashed using SHA256. The data is then packaged and encrypted with the enterprise public key. The business code (which is an idempotent ID) is used as the key of the map. The encrypted data and hash are written to the blockchain as a JSON array [{ciphertext:, resume:, id:}] with the value. Once a new block is confirmed to be generated, it is synchronized to all nodes.

[0038] The query and tracing of business traces can be achieved by retrieving relevant data stored in the block through business code. Any operation can be viewed in the array, and the digest of the existing business data of the business system can be compared with the digest on the block. If there is a difference, it indicates that tampering has occurred. At the same time, the entire process of each business operation can be traced. Furthermore, after decrypting the data in the block with the enterprise private key, specific business data items can be viewed, including information such as the operator, operation content, and time-related business information.

[0039] like Figure 2 As shown, on the other hand, embodiments of the present invention also provide an evidence-based system for business compliance management of financial and tax business systems. The system includes: an information collection module, a data preprocessing and cleaning module, a data analysis module, an alarm module, a blockchain node, a query and traceability module, and a business database.

[0040] The information collection module is used to intercept data streams from the main business processes of the financial and tax business system through dynamic proxies and collect relevant data.

[0041] The data preprocessing and cleaning module is used to preprocess the collected data, obtain standardized data, extract user access IP, user information, department, operation time, operation duration, operation content, idle time, access method, permissions, and enterprise type to construct a multi-dimensional feature vector and normalize it. Weights are assigned to each dimension of the data to calculate the data vector, and the obtained data vector is stored in correspondence with the trace data.

[0042] The data analysis module is used to calculate a reasonable probability range based on several weighted information, including enterprise type, user department information, and permissions, to obtain the reasonable probability for each data vector. It also generates a corresponding reasonable vector based on the user, department, enterprise type, and permissions. Finally, it calculates the distance between the data vectors and the reasonable vectors.

[0043] The alarm module issues a warning when the data is outside the normal range;

[0044] The blockchain nodes are used to store relevant trace information;

[0045] The query and traceability module is used for querying and tracing business traces. It obtains relevant data stored in the block through business code, views any operation in the array, compares the digest of the existing operation business data of the business system with the digest on the block, and if there is a difference, it indicates that there has been tampering. At the same time, it can trace the entire process of each business operation. After decrypting the data in the block with the enterprise private key, it can view the specific business data items.

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

[0047] This invention utilizes technologies such as dynamic proxies to collect traces of activities in financial and tax operations, including original document verification, reimbursement, auditing, voucher generation, bookkeeping, and tax declaration, and then cleans and analyzes the data in a data processing module to obtain reliable and reasonable data. Using blockchain technology, the relevant reliable trace data is submitted to the blockchain and automatically calculated and stored through smart contracts. This ensures the authenticity, accuracy, and compliance of financial activities, allowing for the traceability and verification of the rationality and reliability of financial data, guaranteeing the integrity and traceability of financial information, preventing data tampering, ensuring traceable digital identity information, and enabling full tracking and recording of business operations to guarantee the authenticity and integrity of business traces.

[0048] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this application are indicated by the claims.

[0049] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this application. This application is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A business compliance management evidence-based method for a finance and tax business system, characterized in that, The method comprises: S1, establishing an enterprise private chain or a franchise chain, and creating an account for a tax business participant; S2, collecting relevant data by dynamically intercepting the data flow of the main business links of the tax business system through a dynamic agent; S3, preprocessing the collected data to obtain standardized data, extracting user access IP, user information, department, operation time, operation duration, operation content, idle duration, access method, permission, enterprise type, constructing a multi-dimensional feature vector, and normalizing, setting the weight of each dimension data to calculate the data vector, and storing the obtained data vector and trace data correspondingly; S4, calculating a reasonable probability range according to the enterprise type, user department information, and permission, obtaining the reasonable probability of each data vector, and obtaining the corresponding reasonable vector according to the user, department, enterprise type, and permission; calculating the distance between the data vector and the reasonable vector, if it is not within the normal range, a warning is given, if it is within the normal range, the relevant trace information of the data vector is submitted to the blockchain through the smart contract.

2. The evidence-based method for business compliance management for a finance and tax business system according to claim 1, wherein, In S1, after creating an account, a corresponding relationship {user id, block account} needs to be established in the database for system identity confirmation.

3. The evidence-based method for business compliance management for a finance and tax business system according to claim 1, wherein, The main business links include business single application, review, original single certificate inspection, transfer of accounting single, approval, generation of vouchers, accounting, and declaration, and after collecting relevant data, the data is sent to the message queue in an asynchronous manner, and the message driving mode is completely decoupled from the tax business system.

4. The evidence-based method for business compliance management for a finance and tax business system according to claim 1, wherein, The preprocessing of the collected data includes further cleaning, integration, and standardization of the original data, and labeling each data with a unique serial number.

5. The evidence-based method for business compliance management for a finance and tax business system according to claim 4, wherein, The calculation of the data vector includes a multi-dimensional feature vector After normalization, the empirical weight As a coefficient, evaluate , and the Dn is stored in correspondence with the trace data {flow number, Dn}.

6. The evidence-based method for business compliance management for a finance and tax business system according to claim 5, wherein, The reasonable probability range calculation includes wherein Di represents the obtained data measurement value, and I represents the enterprise industry experience value.

7. The evidence-based method for business compliance management for a finance and tax business system according to claim 1, wherein, The calculation process of the reasonable vector includes: According to the enterprise historical data, K cluster classes are divided The corresponding relationship of the user number, department number, enterprise type code and permission number is Ck, and a reasonable vector Ck is obtained through the user number, department number, enterprise type code and permission number.

8. The evidence-based method for business compliance management for a finance and tax business system according to claim 1, wherein, The distance calculation includes distance calculation between the data measurement value Di and the reasonable vector Ck If T is smaller than the reasonable probability after the calculation value is normalized, it is judged as valid.

9. The evidence-based method for business compliance management for a finance and tax business system according to claim 1, wherein, 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, authorizing the subsequent business, otherwise, returning the error information to the client, after authorization, calculating the data generated by the business operation by sha256 summary, and packaging the data for encryption with the enterprise public key, the business code as the key value of the map, the encrypted data and the summary as the value in the form of json array [{ciphertext:, resume:, id:}], and writing into the blockchain.

10. A tax administration system business process trace management system for implementing the method of any one of claims 1 to 9, characterized by The system comprises: an information collection module, a data preprocessing and cleaning module, a data analysis module, an alarm module, a blockchain node, a query and trace module, and a business database; The information collection module is used to intercept the data flow of the main business links of the tax business system through a dynamic agent, and collect relevant data; The data preprocessing and cleaning module is used to preprocess the collected data to obtain standardized data, extract user access IP, user information, department, operation time, operation duration, operation content, idle duration, access method, permission, enterprise type, construct a multi-dimensional feature vector, and normalize, set the weight of each dimension data to calculate the data vector, and store the obtained data vector and trace data correspondingly; The data analysis module is used for calculating a reasonable probability range according to the enterprise type, user department information and permission, obtaining a reasonable probability of each data vector, and obtaining a corresponding reasonable vector according to the user, department, enterprise type and permission; distance calculation is performed on the data vector and the reasonable vector, The alarm module warns when the data is not in the normal range; The blockchain node is used for storing relevant trace information; The query and trace module is used for querying and tracing business traces, obtaining relevant data stored in the block through a business code, viewing any operation in the array, performing comparison between a summary of business data sha256 of an existing operation of a business system and a summary on the block, and if there is a difference, it indicates that there is tampering, and the whole process of each business operation can be traced, and the specific business data item is viewed after the data of the block is decrypted by the enterprise private key; The business database is used for providing data of the financial and tax business system.

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