Enterprise financial examination system based on big data
The enterprise financial audit system based on big data solves the problems of low data integration efficiency and poor accuracy in existing technologies, and realizes real-time data synchronization, dynamic early warning, accurate prediction and automated audit, thereby improving the efficiency of enterprise financial management and the accuracy of decision-making.
Patent Information
- Application Number
- CN202512019875.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-30
- Publication Date
- 2026-03-20
AI Technical Summary
Existing corporate financial audit systems suffer from inefficiencies in data integration, are prone to human error, lack timeliness, and struggle to guarantee data consistency and integrity, thus failing to meet the needs of enterprises for efficient, accurate, and comprehensive financial audits in the digital age.
The enterprise financial audit system adopts big data and builds a real-time data pipeline through Apache Kafka and Flink to break down data barriers between ERP, CRM and supply chain systems. Combined with intelligent analysis modules and application service modules, it realizes real-time synchronization and seamless flow of business data across the entire chain. By utilizing modules such as dynamic threshold early warning, risk assessment, predictive analysis, automated auditing, visualized decision-making and compliance management, it improves data compliance and decision-making accuracy.
It enables real-time synchronization and consistency of enterprise financial data, dynamic threshold early warning and risk assessment, accurate cash flow and profit forecasting, automated auditing and compliance management, significantly improving financial management efficiency and decision-making accuracy, and reducing human error rate and duplicate audit costs.
Smart Images
Figure CN121707760A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of financial auditing technology, and in particular to a big data-based enterprise financial auditing system. Background Technology
[0002] In today's digital age, enterprises face an increasingly complex and volatile financial environment. As enterprises expand and their businesses diversify, the amount of financial data generated is exploding, and the number of business systems involved is also increasing, such as ERP systems, CRM systems, supply chain systems, and financial systems. These systems operate independently and store key financial and business data from different stages, but there are often data barriers between them, which leads to poor information flow and makes it difficult to effectively guarantee the consistency and integrity of the data. Traditional corporate financial auditing methods mainly rely on manual operation and periodic report analysis. This approach is not only inefficient but also prone to human error. When dealing with massive amounts of financial data, it is difficult for humans to quickly and accurately complete the data integration, cleaning, and analysis, which greatly reduces the timeliness of financial auditing. Due to the lack of a real-time data synchronization mechanism, there is often a disconnect between financial data and business data, which fails to reflect the actual operating status of the company in a timely manner, thereby affecting the management's ability to make accurate decisions. In summary, existing corporate financial audit systems have many shortcomings in data integration and cannot meet the needs of enterprises for efficient, accurate, and comprehensive financial audits in the digital age. Therefore, developing a big data-based corporate financial audit system is of great practical significance. By providing enterprises with comprehensive and multi-level financial audit services, it can improve their financial management level and decision-making efficiency, and reduce financial and compliance risks. Summary of the Invention
[0003] The purpose of this invention is to address the shortcomings of existing technologies by proposing a big data-based enterprise financial audit system.
[0004] To achieve the above objectives, the present invention adopts the following technical solution: A big data-based enterprise financial audit system includes a financial data processing module, an intelligent analysis module, and an application service module; The financial data processing module builds a real-time data pipeline using Apache Kafka or Flink, breaking down data barriers between ERP, CRM, supply chain systems, and financial systems to achieve real-time synchronization and seamless flow of business data across the entire chain, and ensures the compliance and quality of financial data through data governance. The intelligent analysis module includes a dynamic threshold early warning module, a risk assessment module, and a predictive analysis module; The application service module includes an automated audit module, a visual decision-making module, and a compliance management module.
[0005] As a further aspect of the present invention: in the financial data processing module, Kafka serves as a message middleware to buffer high-concurrency data streams, while Flink performs real-time cleaning and computation; by tracing the entire financial data chain, metadata management is used to define the calculation rules, affiliated departments, and compliance tags for financial indicators, thereby verifying the consistency and integrity of financial data; during data storage and analysis, a financial data lake and high-performance queries are used to process structured and unstructured data separately.
[0006] As a further aspect of the present invention: the dynamic threshold early warning module processes high-concurrency real-time transaction data based on Spark Streaming, defines dynamic threshold rules through the Drools rule engine, supports flexible configuration of business rules, and achieves millisecond-level response.
[0007] As a further aspect of the present invention: the risk assessment module constructs a risk assessment system by integrating two types of indicators: financial and governance structure. The financial aspect includes indicators such as debt-to-equity ratio, current ratio, and accounts receivable turnover days. It is trained using the XGBoost algorithm based on a gradient boosting tree framework and uses feature importance analysis to screen key risk factors. In financial application scenarios, it is used for supplier credit assessment and customer default prediction.
[0008] As a further aspect of this invention: the predictive analysis module achieves accurate prediction of cash flow and profit through the collaborative use of Prophet and LSTM algorithms; Prophet automatically detects the periodic patterns of seasonal financial data based on an additivity model, while LSTM captures the dynamic correlation between nonlinear macroeconomic indicators and financial data through gated recurrent units and multilayer neural networks; in cash flow prediction, historical cash flow data, sales order forecasts, and macroeconomic variables are integrated to output a trend chart for the next 3 months and mark potential funding gaps; profit prediction integrates supply chain cost data, Prophet seasonal revenue forecasts, and macroeconomic indicators such as industry growth rates to output profits and confidence intervals for the next 12 months.
[0009] As a further aspect of the present invention: the automated audit module constructs a trusted audit chain through RPA and blockchain technology; the RPA robot uses OCR technology to intelligently extract key information from invoices, automatically matches bank statements with system transaction records and marks discrepancies, and generates audit working papers according to preset templates; the blockchain technology stores the hash value of the RPA operation log in the consortium blockchain to ensure that the audit trail is tamper-proof and supports the sharing of on-chain data among suppliers, auditors, and enterprises to reduce duplicate audits.
[0010] As a further aspect of this invention: the visualization decision-making module integrates a data lake and real-time data stream through Tableau / Power BI to construct a three-level analysis architecture that supports dynamic drill-down; the first-level view presents global indicators such as total annual profit, the second-level view allows drilling down to departmental contribution distribution, and the third-level view allows penetration to specific transaction records; by creating a hierarchical data model and configuring intelligent filters and drill-down buttons, real-time data updates are achieved, enabling data exploration to be completed autonomously without IT intervention.
[0011] As a further aspect of the present invention: the compliance management module relies on NLP technology to achieve intelligent matching between regulations and transactions; it parses regulatory clauses through the BERT model, scans transaction data in real time and calculates the similarity with regulatory clauses, and automatically marks high-risk transactions when the similarity exceeds a threshold. At the same time, it generates a PDF compliance report containing the matching results and links to the original text of the regulations in accordance with regulatory requirements and pushes it to the legal department.
[0012] As a further aspect of the present invention: the implementation steps of the predictive analysis module include extracting financial data from the data lake, accessing third-party APIs to obtain macroeconomic data, performing time series decomposition and periodic modeling through Prophet, training a nonlinear relationship model with LSTM, and finally generating a visualization report and marking key risk points.
[0013] As a further aspect of this invention: the automated audit module implementation process encompasses RPA collecting transaction data from the ERP system, OCR parsing of invoice images, automatic reconciliation and push of discrepancies for review, and finally, storing the audit operation logs and reconciliation result hash values on the blockchain for evidence, with the audit report including the on-chain evidence number. Compared with existing technologies, the present invention provides a big data-based enterprise financial audit system, which has the following beneficial effects: 1. The financial data processing module breaks down data barriers between ERP, CRM, supply chain systems, and financial systems. By using Kafka and Flink to build a real-time data pipeline, it achieves real-time synchronization and seamless flow of business data across the entire chain, effectively ensuring the consistency of financial and business data. At the same time, the data governance mechanism ensures the compliance and quality of financial data. Enterprises can make decisions based on accurate and timely data, greatly shortening the decision-making cycle and improving the accuracy and timeliness of decisions.
[0014] 2. The dynamic threshold early warning module and risk assessment module in the intelligent analysis module provide enterprises with powerful risk control capabilities. The dynamic threshold early warning module, based on the Spark Streaming and Drools rule engine, can process high-concurrency transaction data in real time, achieve millisecond-level response, and issue early warnings in a timely manner according to preset rules, helping enterprises quickly identify potential risks. The risk assessment module integrates two types of indicators, financial and governance structure, to build a risk assessment system. It uses the XGBoost algorithm to screen key risk factors and plays an important role in scenarios such as supplier credit assessment and customer default prediction, effectively reducing the enterprise's credit risk and default risk.
[0015] 3. The predictive analysis module, through the collaboration of Prophet and LSTM algorithms, achieves accurate prediction of cash flow and profit. In cash flow prediction, the module integrates multiple data sources, outputs a trend chart for the next 3 months, and marks potential funding gaps; profit prediction integrates multiple data sources, outputting profits and confidence intervals for the next 12 months. Based on these accurate prediction results, enterprises can formulate financial planning and business strategies in advance, avoid funding shortages or waste, and provide forward-looking decision support for enterprise development.
[0016] 4. The automated auditing module in the application service module constructs a trusted audit chain through RPA and blockchain technology. The RPA robot uses OCR technology to intelligently extract key information from invoices, automatically matches bank statements with system transaction records and marks discrepancies, and generates audit working papers according to preset templates. Blockchain technology stores the hash value of the RPA operation log in the consortium blockchain to ensure that the audit trail is tamper-proof and supports three-party sharing of on-chain data, reducing the workload and error rate of manual auditing, improving audit efficiency and accuracy, and reducing the cost of repeated audits.
[0017] 5. The Visualization Decision Module integrates a data lake and real-time data stream through Tableau / Power BI, constructing a three-tiered analytical architecture that supports dynamic drill-down. This enables autonomous data exploration without IT intervention, reducing decision response time by 50% and quickly locating anomalies, significantly improving financial insight efficiency and problem tracing capabilities. The Compliance Management Module leverages NLP technology to intelligently match regulations with transactions. It uses a BERT model to parse regulatory clauses, scans transaction data in real time, marks high-risk transactions, and automatically generates compliance reports, which are then pushed to the legal department. This allows companies to gain a more intuitive understanding of their business situation in financial management, promptly identify problems, and take corrective action, while ensuring that the company's operations comply with regulatory requirements.
[0018] The parts of this device not covered herein are the same as or can be implemented using existing technologies. This invention has a simple structure and is easy to operate. Attached Figure Description
[0019] Figure 1 This is an overall block diagram of a big data-based enterprise financial audit system proposed in this invention; Figure 2 This is an overall diagram of the intelligent analysis module of a big data-based enterprise financial audit system proposed in this invention; Figure 3 This is an overall diagram of the application analysis module of a big data-based enterprise financial audit system proposed in this invention. Detailed Implementation
[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.
[0021] A big data-based enterprise financial audit system includes a financial data processing module, an intelligent analysis module, and an application service module; The financial data processing module uses Apache Kafka or Flink to build a real-time data pipeline, breaking down data barriers between ERP (such as SAP), CRM (such as Salesforce), supply chain systems (such as WMS) and financial systems. This enables real-time synchronization and seamless flow of business data across the entire chain, including order generation (ERP), payment records (financial system), invoice issuance (CRM), and logistics status (supply chain), ensuring consistency between financial and business data and timely decision-making. Kafka acts as a message middleware to buffer high-concurrency data streams, while Flink performs real-time cleaning and computation. The financial data processing module ensures the compliance and quality of financial data through data governance. By tracking the entire financial data chain, metadata management defines the calculation rules, departmental affiliation, and compliance tags for financial indicators, verifying the consistency and integrity of financial data. Data storage and analysis utilize a financial data lake and high-performance queries for structured data, such as general ledgers and subsidiary ledgers in ERP systems, and cost data in the supply chain. For unstructured data, it uses scanned contracts and audit logs. The intelligent analysis module includes: The dynamic threshold alert module, based on Spark Streaming, is used to process high-concurrency real-time transaction data (such as orders and payment records) and achieve millisecond-level response. It uses the Drools rule engine to define dynamic threshold rules (such as "trigger alert for large transactions exceeding 5% of daily revenue"), supporting flexible configuration of business rules. The risk assessment module constructs a risk assessment system by integrating two types of indicators: financial and governance structure. The financial aspect includes indicators such as debt-to-equity ratio, current ratio, and accounts receivable turnover days. It uses the XGBoost algorithm based on a gradient boosting tree framework for training and uses feature importance analysis to screen key risk factors (such as the combination of "low governance structure score + high financial leverage" which increases the enterprise's risk probability by 30%). In financial application scenarios, this module can be used for supplier credit assessment (combining supplier financial data such as "accounts receivable turnover days > 90 days" with governance data such as "no independent directors on the board" to output risk level) and customer default prediction (integrating customer historical order data with macroeconomic indicators such as industry PMI to predict the probability of default in the next 6 months). The predictive analytics module uses Prophet and LSTM algorithms to accurately predict cash flow and profits. Prophet automatically detects the cyclical patterns in seasonal financial data based on an additive model, while LSTM captures the dynamic correlation between nonlinear macroeconomic indicators and financial data through gated recurrent units and multi-layer neural networks. In cash flow forecasting, the module integrates historical cash flow data, sales order forecasts, and macroeconomic variables to output a trend chart for the next three months and mark potential funding gaps. Profit forecasting integrates supply chain cost data, Prophet's seasonal revenue forecasts, and macroeconomic indicators such as industry growth rates to output profits and confidence intervals for the next 12 months. Implementation steps include extracting financial data from a data lake (Hive / HBase), accessing third-party APIs to obtain macroeconomic data, performing time series decomposition and cyclical modeling using Prophet, training a nonlinear relationship model using LSTM, and finally generating a visual report and marking key risk points to provide enterprises with forward-looking decision support. The application service module includes: The automated audit module constructs a trusted audit chain through RPA and blockchain technology. The RPA robot uses OCR technology to intelligently extract key information from invoices, automatically matches bank statements with system transaction records and marks discrepancies, and generates audit working papers according to preset templates. Blockchain technology stores the hash value of RPA operation logs on the consortium blockchain to ensure that the audit trail is tamper-proof and supports the sharing of on-chain data among suppliers, auditors, and enterprises to reduce duplicate audits. The implementation process covers RPA collecting transaction data from the ERP system, OCR parsing invoice images, automatic reconciliation and push of discrepancies for review, and finally storing the audit operation logs and reconciliation result hash values on the blockchain for evidence, with the audit report including the on-chain evidence number. The visualization decision-making module integrates a data lake (Hive / HBase) and a real-time data stream (Kafka) through Tableau / Power BI to build a three-level analysis architecture that supports dynamic drill-down: the first-level view presents global indicators such as total annual profit, the second-level view allows drilling down to the departmental contribution distribution, and the third-level view allows penetrating to specific transaction records; By creating a hierarchical data model of "annual → quarterly → department → transaction" and configuring intelligent filters and drill-down buttons, real-time data refresh based on Kafka can be achieved every 15 minutes. It can autonomously complete data exploration without IT intervention, reducing decision response time by 50%, and can quickly locate anomalies through three-level penetration, significantly improving the efficiency of financial insight and the ability to trace the source of problems; The compliance management module leverages NLP technology to intelligently match regulations with transactions: it uses a BERT model to parse regulatory clauses, scans transaction data in real time and calculates the similarity to regulatory clauses, automatically flagging high-risk transactions when the similarity exceeds a threshold, and simultaneously generating a PDF compliance report containing the matching results and links to the original regulatory text, as required by regulations, and pushing it to the legal department. The implementation process covers regulatory database construction, transaction matching, and report generation.
[0022] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.
Claims
1. A big data-based enterprise financial audit system, characterized in that, It includes a financial data processing module, an intelligent analysis module, and an application service module; The financial data processing module builds a real-time data pipeline using Apache Kafka or Flink, breaking down data barriers between ERP, CRM, supply chain systems, and financial systems to achieve real-time synchronization and seamless flow of business data across the entire chain, and ensures the compliance and quality of financial data through data governance. The intelligent analysis module includes a dynamic threshold early warning module, a risk assessment module, and a predictive analysis module; The application service module includes an automated audit module, a visual decision-making module, and a compliance management module.
2. The enterprise financial audit system based on big data according to claim 1, characterized in that, In the financial data processing module, Kafka serves as a message middleware to buffer high-concurrency data streams, while Flink performs real-time cleaning and computation. By tracing the entire financial data chain, metadata management is used to define the calculation rules, affiliated departments, and compliance tags for financial indicators, thereby verifying the consistency and integrity of financial data. When storing and analyzing data, we utilize a financial data lake and high-performance queries to process structured and unstructured data separately.
3. The enterprise financial audit system based on big data according to claim 1, characterized in that, The dynamic threshold early warning module is based on Spark Streaming to process high-concurrency real-time transaction data. It defines dynamic threshold rules through the Drools rule engine, supports flexible configuration of business rules, and achieves millisecond-level response.
4. The enterprise financial audit system based on big data according to claim 1, characterized in that, The risk assessment module constructs a risk assessment system by integrating two types of indicators: financial and governance structure. The financial aspect includes indicators such as debt-to-equity ratio, current ratio, and accounts receivable turnover days. It is trained using the XGBoost algorithm based on a gradient boosting tree framework and uses feature importance analysis to screen key risk factors. In financial application scenarios, it is used for supplier credit assessment and customer default prediction.
5. The enterprise financial audit system based on big data according to claim 1, characterized in that, The predictive analysis module achieves accurate prediction of cash flow and profit through the collaborative use of Prophet and LSTM algorithms. Prophet automatically detects the periodic patterns of seasonal financial data based on an additivity model, while LSTM captures the dynamic correlation between nonlinear macroeconomic indicators and financial data through gated recurrent units and multi-layer neural networks. In cash flow prediction, historical cash flow data, sales order forecasts, and macroeconomic variables are integrated to output a trend chart for the next three months and mark potential funding gaps. Profit prediction integrates supply chain cost data, Prophet seasonal revenue forecasts, and macroeconomic indicators such as industry growth rates to output profits and confidence intervals for the next 12 months.
6. The enterprise financial audit system based on big data according to claim 1, characterized in that, The automated audit module constructs a trusted audit chain through RPA and blockchain technology; the RPA robot uses OCR technology to intelligently extract key information from invoices, automatically matches bank statements with system transaction records and marks discrepancies, and generates audit working papers according to preset templates; the blockchain technology stores the hash value of the RPA operation log in the consortium blockchain to ensure that the audit trail is tamper-proof and supports the sharing of on-chain data among suppliers, auditors and enterprises to reduce duplicate audits.
7. The enterprise financial audit system based on big data according to claim 1, characterized in that, The visualization decision-making module integrates a data lake and real-time data stream through Tableau / Power BI to build a three-level analysis architecture that supports dynamic drill-down. The first-level view presents global indicators such as total annual profit, the second-level view allows drilling down to the departmental contribution distribution, and the third-level view allows penetration to specific transaction records. By creating a hierarchical data model and configuring smart filters and drill-down buttons, it can achieve real-time data refresh and autonomously complete data exploration without IT intervention.
8. The enterprise financial audit system based on big data according to claim 1, characterized in that, The compliance management module relies on NLP technology to achieve intelligent matching between regulations and transactions; it uses the BERT model to parse regulatory clauses, scans transaction data in real time and calculates the similarity with regulatory clauses, and automatically marks high-risk transactions when the similarity exceeds a threshold. At the same time, it generates a PDF compliance report containing the matching results and links to the original regulatory text as required by regulations and pushes it to the legal department.
9. A big data-based enterprise financial audit system according to claim 5, characterized in that, The predictive analytics module implementation steps include extracting financial data from the data lake, accessing third-party APIs to obtain macroeconomic data, performing time series decomposition and periodic modeling using Prophet, training a nonlinear relationship model using LSTM, and finally generating a visualization report and marking key risk points.
10. A big data-based enterprise financial audit system according to claim 6, characterized in that, The automated audit module implementation process covers RPA to collect transaction data from the ERP system, OCR to parse invoice images, automatic reconciliation and push of discrepancies for review, and finally, the audit operation log and reconciliation result hash value are stored on the blockchain for evidence, and the audit report is accompanied by the on-chain evidence number.