Financial data automatic acquisition and processing system

Abstract

The invention relates to the technical field of financial data acquisition and processing, and provides an automatic financial data acquisition and processing system which comprises a data acquisition module, an intelligent identification module, a data cleaning module, an intelligent classification module, a real-time processing engine and a data fusion module. According to the financial data automatic acquisition and processing system and method, the modern information technology and the artificial intelligence technology are comprehensively utilized, full-process automation and intelligentization of financial data acquisition, identification, cleaning, classification, fusion, real-time processing and risk early warning are achieved, the efficiency, accuracy and safety of financial data processing are greatly improved, and the financial data automatic acquisition and processing system and method are suitable for popularization and application. The method has the advantages that the labor cost is reduced, the financial management process is optimized, the risk control capability and decision support capability of the enterprise are enhanced, the digital transformation and intelligent upgrading of the financial management of the enterprise are promoted, and the method has wide application prospect and popularization value.

Description

Technical Field

[0001] This invention relates to the field of financial data acquisition and processing technology, specifically to an automated financial data acquisition and processing system. Background Technology

[0002] With the rapid development of information technology and the continuous advancement of enterprise digital transformation, financial data is playing an increasingly important role in enterprise management. Traditional financial data processing methods mainly rely on manual entry, manual verification, and gradual generation of financial statements. This is not only inefficient but also prone to data errors and omissions, making it difficult to meet enterprises' needs for real-time and accurate financial information.

[0003] In practice, corporate financial information comes from diverse and dispersed sources, including multiple heterogeneous data sources such as ERP systems, bank statements, electronic invoices, and tax filing systems. This data often has varying formats and complex structures, making centralized manual processing difficult and severely hindering the automation and intelligence of financial management. Simultaneously, unstructured vouchers (such as scanned invoices and handwritten vouchers) are difficult to utilize directly, and traditional identification methods suffer from low recognition rates and inefficiencies.

[0004] In recent years, the rapid development of technologies such as OCR (Optical Character Recognition), Natural Language Processing, Machine Learning, and Deep Learning has provided a new technological foundation for the automatic identification, processing, and analysis of financial data. Utilizing these advanced technologies, automatic identification of financial vouchers, automatic data cleaning, and intelligent classification can be achieved, thereby significantly improving the efficiency and accuracy of financial data processing.

[0005] However, the market currently lacks a complete solution that integrates multi-source heterogeneous data acquisition, unstructured voucher recognition, data cleaning, intelligent classification, real-time processing, and risk warning. Most systems have limited functionality or only support certain aspects, making it difficult to meet enterprises' comprehensive needs for automation, intelligence, and security of financial data.

[0006] Therefore, this solution proposes an automated financial data collection and processing system to address the aforementioned issues. Summary of the Invention

[0007] To overcome the shortcomings of the existing technology, the purpose of this invention is to provide an automated financial data collection and processing system.

[0008] To achieve the aforementioned objective, the technical solution of the present invention is implemented as follows: an automated financial data collection and processing system, comprising:

[0009] The data acquisition module is used to automatically collect internal and external financial data of an enterprise through multi-source heterogeneous interfaces, including but not limited to ERP system data, bank transaction data, invoice data and tax declaration data;

[0010] The intelligent recognition module uses OCR technology and natural language processing algorithms to automatically recognize and convert unstructured financial documents into structured forms.

[0011] The data cleaning module automatically detects and corrects abnormal, duplicate, and missing data based on a preset rule base and machine learning algorithms.

[0012] The intelligent classification module uses a deep learning model to automatically classify and label financial data;

[0013] The real-time processing engine adopts a streaming computing architecture to enable real-time processing and dynamic updating of financial data;

[0014] The data fusion module standardizes and merges heterogeneous data from multiple sources to form a unified financial data view.

[0015] Preferably, the data acquisition module includes:

[0016] API interface adapter, supporting dynamic adaptation of multiple interface protocols such as REST and SOAP;

[0017] The scheduled task generator automatically triggers data acquisition tasks according to preset strategies.

[0018] The incremental data acquisition engine uses timestamps and version numbers to achieve incremental data synchronization.

[0019] The data cache pool uses distributed caching technology to improve data collection efficiency.

[0020] Preferably, the intelligent recognition module includes:

[0021] The image preprocessing unit performs noise reduction, tilt correction, and enhancement processing on the financial voucher images;

[0022] A multi-model fusion recognition engine combines CNN and RNN models to improve recognition accuracy.

[0023] The semantic understanding unit understands financial terms and contextual relationships based on a pre-trained language model.

[0024] The confidence evaluator scores the recognition results on their credibility and marks them for manual verification.

[0025] Preferably, the data cleaning module includes:

[0026] An anomaly detection algorithm library, containing various anomaly detection algorithms based on statistics, machine learning, and business rules;

[0027] An automatic repair engine automatically repairs outliers based on historical data patterns and business logic.

[0028] A data quality scoring system monitors and evaluates data quality indicators in real time.

[0029] Audit trail logs record detailed information about all data cleaning operations.

[0030] Preferably, it further includes:

[0031] The intelligent early warning module provides real-time early warnings of financial risks based on preset thresholds and machine learning models.

[0032] The visualization module displays financial data analysis results through interactive charts and dashboards;

[0033] The access control module enables fine-grained data access control based on roles.

[0034] The data encryption module uses national cryptographic algorithms to encrypt and store sensitive financial data during transmission.

[0035] Preferably, the intelligent early warning module includes:

[0036] The risk indicator library covers multiple dimensions of financial risk indicators, including liquidity, solvency, and profitability.

[0037] A dynamic threshold learner that automatically learns and adjusts warning thresholds based on historical data;

[0038] The correlation analysis engine identifies abnormal correlations between different financial indicators;

[0039] The early warning push service pushes early warning information through multiple channels such as email, SMS, and system notifications.

[0040] Preferably, the real-time processing engine employs:

[0041] A distributed stream processing framework that supports horizontal scaling and high concurrency processing;

[0042] In-memory computing technology improves the processing speed of complex financial calculations;

[0043] An event-driven architecture enables real-time response and processing of financial events;

[0044] The breakpoint resume mechanism ensures the reliability and integrity of data processing.

[0045] Preferably, it further includes:

[0046] The blockchain evidence storage module stores key financial data and operation records on the blockchain to ensure that the data cannot be tampered with.

[0047] The intelligent compliance check module automatically checks whether financial data complies with relevant laws and accounting standards.

[0048] The multilingual support module supports the recognition and processing of financial terms in multiple languages, including Chinese and English.

[0049] Preferably, a method for automated collection and processing of financial data includes the following steps:

[0050] S1: Configure data source connection parameters to establish a secure connection with various financial systems;

[0051] S2: Start the automated data collection task and collect multi-source financial data according to preset rules;

[0052] S3: Perform intelligent identification and structured processing on the collected raw data;

[0053] S4: Perform data cleaning procedures to improve data quality;

[0054] S5: Use intelligent algorithms to classify and label the cleaned data;

[0055] S6: Performs data calculations and analysis through a real-time processing engine;

[0056] S7: Visualize the processing results and trigger the corresponding early warning mechanism.

[0057] Preferably, a computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of claim 9.

[0058] The beneficial effects of this invention are reflected in:

[0059] The financial data automated collection and processing system and method of this invention comprehensively utilizes modern information technology and artificial intelligence technology to achieve full-process automation and intelligence in financial data collection, identification, cleaning, classification, fusion, real-time processing and risk warning. It greatly improves the efficiency, accuracy and security of financial data processing, reduces labor costs, optimizes financial management processes, enhances enterprises' risk control capabilities and decision support capabilities, promotes the digital transformation and intelligent upgrading of enterprise financial management, and has broad application prospects and promotion value. Attached Figure Description

[0060] In the attached diagram:

[0061] Figure 1 This is a schematic diagram of the system architecture of the present invention;

[0062] Figure 2 This is a schematic diagram of the method steps of the present invention. Detailed Implementation

[0063] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. Obviously, the described embodiments are only a part of the embodiments of the invention, and not all of them. Unless otherwise specified, the embodiments and features described in this application can be combined with each other. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0064] Furthermore, "multiple" refers to two or more. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of a person skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the invention.

[0065] Please refer to the instruction manual appendix. Figures 1-2 This invention provides an automated financial data acquisition and processing system:

[0066] Example 1

[0067] This embodiment provides an automated financial data acquisition and processing system, which includes a data acquisition module, an intelligent identification module, a data cleaning module, an intelligent classification module, a real-time processing engine, and a data fusion module.

[0068] The data acquisition module is used to automatically collect internal and external financial data of an enterprise through multi-source heterogeneous interfaces. In this embodiment, the module can simultaneously connect to the enterprise's SAP ERP system, Oracle financial system, corporate online banking interfaces of major banks, the tax bureau's electronic filing system, and electronic invoice platform. For example, the system can automatically download the previous day's bank statements from the Industrial and Commercial Bank of China's corporate online banking at 2:00 AM every day, and obtain the previous month's tax return data from the tax system on the 1st of each month.

[0069] Specifically, the data acquisition module includes an API interface adapter that supports multiple interface protocols such as REST, SOAP, and WebService, and can dynamically adjust request parameters and data formats according to the interface specifications of different data sources. The scheduled task operator uses the Quartz scheduling framework, supporting the setting of acquisition tasks on various periods such as minutes, hours, days, weeks, and months. The incremental acquisition engine records the last timestamp or data version number of each acquisition, retrieving only newly added or modified data, significantly reducing data transmission volume. The data caching pool uses a Redis distributed cache to cache frequently accessed basic data in memory, improving system response speed.

[0070] The intelligent recognition module employs OCR technology and natural language processing algorithms to automatically recognize unstructured financial documents. Its image preprocessing unit uses the OpenCV library for image denoising, binarization, and tilt correction to improve subsequent recognition accuracy. The multi-model fusion recognition engine combines convolutional neural networks (CNNs) for image feature extraction and recurrent neural networks (RNNs) for sequence modeling, achieving an accuracy rate of over 98%. The semantic understanding unit, based on a BERT pre-trained model, understands the semantic relationships between financial terms such as "accounts receivable" and "prepayments." A confidence evaluator assigns a confidence score of 0-100 to each recognition result; results below 85 are marked as requiring manual review.

[0071] The data cleaning module is responsible for improving data quality. The anomaly detection algorithm library integrates various anomaly detection algorithms such as the 3σ criterion, box plots, isolation forests, and LSTM, which can identify numerical anomalies, format anomalies, and logical anomalies. The automatic repair engine can automatically repair common errors such as inconsistent date formats and inconsistent monetary units; for complex errors, it generates repair suggestions for manual confirmation. The data quality scoring system evaluates data quality from four dimensions: completeness, accuracy, consistency, and timeliness, generating a comprehensive score from 0 to 100. The audit trail log records detailed information such as the modification time, the modifier, and the values ​​before and after the modification for each piece of data, ensuring the traceability of the data processing process.

[0072] The intelligent classification module uses deep learning models to automatically classify financial data. For example, the system can automatically classify bank statements into categories such as "sales revenue," "purchase payments," "salary expenses," and "tax payments," with an accuracy rate of over 95%.

[0073] The real-time processing engine utilizes the Apache Flink streaming computing framework. Distributed stream processing can scale horizontally to dozens of nodes, achieving a processing capacity of millions of records per second. In-memory computing uses the Apache Ignite in-memory data grid to cache the calculation results of commonly used financial indicators in memory. The event-driven architecture immediately triggers corresponding processing flows upon detecting events such as large fund flows or abnormal transactions. A breakpoint resumption mechanism ensures that after an abnormal system interruption, processing can resume from the last point, ensuring no data loss.

[0074] The data fusion module standardizes and integrates data from different systems. For example, it matches "customer codes" in the ERP system with "payer accounts" in the banking system to form complete customer payment records.

[0075] Example 2

[0076] Based on Embodiment 1, the system in this embodiment also includes an intelligent early warning module, a visualization display module, an access control module, and a data encryption module.

[0077] The intelligent early warning module's risk indicator library includes over 50 pre-set financial risk indicators such as current ratio, quick ratio, debt-to-equity ratio, and return on equity. The dynamic threshold learner uses machine learning algorithms to automatically calculate reasonable ranges for each indicator based on three years of historical financial data from the company. The correlation analysis engine uses the Apriori algorithm to discover correlation rules between indicators, such as "when accounts receivable turnover decreases, the risk of bad debts increases." The early warning push service supports tiered alerts; general alerts are pushed via system messages, while severe alerts are notified simultaneously via SMS and telephone.

[0078] The visualization module offers a wealth of chart display functions, including financial statements, trend analysis charts, comparative analysis charts, budget execution status, etc., and supports drag-and-drop custom dashboards.

[0079] The access control module implements RBAC-based access control, down to the field level. For example, a regular accountant can only view voucher information, a finance manager can view all financial data, and the general manager can also view financial analysis reports.

[0080] The data encryption module uses the national standard SM4 algorithm to encrypt sensitive data, and the key is managed using the SM2 algorithm to ensure the security of financial data.

[0081] The system also includes a blockchain evidence storage module, which uploads the hash values ​​of key financial data to the blockchain to create an immutable evidence record. Every important transaction and every financial statement generated leaves a trace on the blockchain, providing a reliable basis for auditing work.

[0082] The intelligent compliance check module incorporates the latest accounting standards and tax regulations, automatically checking whether financial processing is compliant. For example, it checks whether revenue recognition complies with the new revenue standards, whether cost accounting complies with regulations, and whether tax processing complies with the latest tax law requirements.

[0083] The multilingual support module not only supports the recognition of Chinese and English financial terms, but can also process financial documents in multiple languages ​​such as Japanese and Korean, meeting the needs of multinational companies.

[0084] Example 3

[0085] This embodiment provides a method for automated collection and processing of financial data, the specific steps of which are as follows:

[0086] The first step involves the system administrator entering connection parameters for each data source through the configuration interface, including server address, port, username, and password. The system automatically tests the connection and saves the configuration. During the configuration process, the system automatically detects the data source type and recommends the most suitable connection method.

[0087] The second step involves the system initiating data collection tasks according to preset collection rules. For example, it might collect the previous day's bank statements at 9:00 AM every day and collect the previous month's invoice data on the 5th of each month. During the collection process, the system monitors the progress in real time. If a network error or data source failure occurs, it will automatically retry and record the error log.

[0088] The third step involves the system performing image enhancement processing on the scanned invoice images, including removing background noise, adjusting contrast, and correcting tilt angles. Then, OCR is used to recognize key information such as the invoice code, invoice number, invoice date, and amount. The recognition results are cross-validated with the electronic invoice information returned by the tax bureau interface to ensure data accuracy.

[0089] The fourth step, data cleaning, involves the system automatically identifying and handling various data quality issues. For duplicate data, the system calculates a data fingerprint using the MD5 algorithm and deletes identical records. For missing data, the system intelligently supplements it according to business rules, such as filling in missing cost data based on historical averages. For anomalous data, such as negative sales figures or discount rates outside the normal range, the system flags the data and generates an anomaly report.

[0090] The fifth step involves automatically classifying the cleaned data using a pre-trained classification model. This model, trained on a large amount of historical financial data, can accurately identify the accounting subject and business type to which each data point belongs and assign it a corresponding label.

[0091] The sixth step involves the real-time processing engine performing various calculations and analyses on the data. This includes real-time calculation of cash flow, updating various financial indicators, and performing trend analysis and forecasting. All calculation results are updated to the data warehouse in real time for subsequent analysis.

[0092] The seventh step involves displaying the processing results in real time on a large visual screen, allowing management to intuitively see the company's financial situation. When anomalies are detected, such as the risk of cash flow disruption, overdue accounts receivable, or abnormally high costs, the system will immediately trigger an early warning mechanism, notifying relevant responsible persons through multiple channels.

[0093] Application Examples

[0094] A large manufacturing company achieved remarkable results after deploying this system. Previously, the company had 30 finance staff who spent a significant amount of time each month on data entry, verification, and report preparation. After deploying the system, the efficiency of financial data collection increased by 80%, and tasks that previously required manual data entry by five finance staff members now only require one person to supervise system operation.

[0095] In terms of data accuracy, the accuracy rate has increased from 95% with manual data entry to 99.5%, significantly reducing financial risks caused by data errors. Particularly in invoice recognition, the system can accurately identify various types of VAT invoices, general invoices, and even handwritten invoices, greatly improving the work efficiency of finance personnel.

[0096] The time for generating financial statements has been shortened from T+5 to T+1, allowing management to understand the company's financial situation more promptly and providing strong support for business decisions. Daily cash flow reports, weekly operational analysis reports, and monthly financial statements can all be generated automatically without manual intervention.

[0097] Through its intelligent early warning function, the company discovered a significant accounts receivable collection risk 15 days in advance, enabling it to take timely collection measures and avoid potential losses of 5 million yuan. The system also helped the company identify several abnormal transactions, including fraudulent invoices and duplicate expense claims, saving the company over 2 million yuan in economic losses.

[0098] In addition, the deployment of the system has brought other positive impacts. Finance personnel are freed from heavy data processing tasks, allowing them to devote more energy to financial analysis and management. The overall level of corporate financial management is improved, internal controls are more robust, and auditing work is more convenient.

[0099] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0100] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0101] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. An automated financial data acquisition and processing system, characterized in that, include: The data acquisition module is used to automatically collect internal and external financial data of an enterprise through multi-source heterogeneous interfaces, including but not limited to ERP system data, bank transaction data, invoice data and tax declaration data; The intelligent recognition module uses OCR technology and natural language processing algorithms to automatically recognize and convert unstructured financial documents into structured forms. The data cleaning module automatically detects and corrects abnormal, duplicate, and missing data based on a preset rule base and machine learning algorithms. The intelligent classification module uses a deep learning model to automatically classify and label financial data; The real-time processing engine adopts a streaming computing architecture to enable real-time processing and dynamic updating of financial data; The data fusion module standardizes and merges heterogeneous data from multiple sources to form a unified financial data view.

2. The automated financial data acquisition and processing system according to claim 1, characterized in that, The data acquisition module includes: API interface adapter, supporting dynamic adaptation of multiple interface protocols such as REST and SOAP; The scheduled task generator automatically triggers data acquisition tasks according to preset strategies. The incremental data acquisition engine uses timestamps and version numbers to achieve incremental data synchronization. The data cache pool uses distributed caching technology to improve data collection efficiency.

3. The automated financial data acquisition and processing system according to claim 1, characterized in that, The intelligent recognition module includes: The image preprocessing unit performs noise reduction, tilt correction, and enhancement processing on the financial voucher images; A multi-model fusion recognition engine combines CNN and RNN models to improve recognition accuracy. The semantic understanding unit understands financial terms and contextual relationships based on a pre-trained language model. The confidence evaluator scores the recognition results on their credibility and marks them for manual verification.

4. The automated financial data acquisition and processing system according to claim 1, characterized in that, The data cleaning module includes: An anomaly detection algorithm library, containing various anomaly detection algorithms based on statistics, machine learning, and business rules; An automatic repair engine automatically repairs outliers based on historical data patterns and business logic. A data quality scoring system monitors and evaluates data quality indicators in real time. Audit trail logs record detailed information about all data cleaning operations.

5. The automated financial data acquisition and processing system according to claim 1, characterized in that, Also includes: The intelligent early warning module provides real-time early warnings of financial risks based on preset thresholds and machine learning models. The visualization module displays financial data analysis results through interactive charts and dashboards; The access control module enables fine-grained data access control based on roles. The data encryption module uses national cryptographic algorithms to encrypt and store sensitive financial data during transmission.

6. The automated financial data acquisition and processing system according to claim 5, characterized in that, The intelligent early warning module includes: The risk indicator library covers multiple dimensions of financial risk indicators, including liquidity, solvency, and profitability. A dynamic threshold learner that automatically learns and adjusts warning thresholds based on historical data; The correlation analysis engine identifies abnormal correlations between different financial indicators; The early warning push service pushes early warning information through multiple channels such as email, SMS, and system notifications.

7. The automated financial data acquisition and processing system according to claim 1, characterized in that, The real-time processing engine employs: A distributed stream processing framework that supports horizontal scaling and high concurrency processing; In-memory computing technology improves the processing speed of complex financial calculations; An event-driven architecture enables real-time response and processing of financial events; The breakpoint resume mechanism ensures the reliability and integrity of data processing.

8. A financial data automated acquisition and processing system according to any one of claims 1-7, characterized in that, Also includes: The blockchain evidence storage module stores key financial data and operation records on the blockchain to ensure that the data cannot be tampered with. The intelligent compliance check module automatically checks whether financial data complies with relevant laws and accounting standards. The multilingual support module supports the recognition and processing of financial terms in multiple languages, including Chinese and English.

9. A method for automated collection and processing of financial data, applied to the system described in any one of claims 1-8, characterized in that, Includes the following steps: S1: Configure data source connection parameters to establish a secure connection with various financial systems; S2: Start the automated data collection task and collect multi-source financial data according to preset rules; S3: Perform intelligent identification and structured processing on the collected raw data; S4: Perform data cleaning procedures to improve data quality; S5: Use intelligent algorithms to classify and label the cleaned data; S6: Performs data calculations and analysis through a real-time processing engine; S7: Visualize the processing results and trigger the corresponding early warning mechanism.

10. A computer-readable storage medium, characterized in that, The device contains a computer program that, when executed by a processor, implements the method of claim 9.

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