Financial data management method and system

Abstract

The invention discloses a financial data management method and system. The system comprises a data acquisition module, a data analysis engine module, a classified storage module, an authority management module, a monitoring and alarm module and a data backup and recovery module. The data acquisition module is used for being in charge of being connected with various financial related systems in an enterprise and an external data source to obtain financial original data; the data analysis engine module is used for analyzing the collected financial data, performing deep analysis by using a data analysis algorithm and a model, and extracting valuable information; in the aspect of the system, the system is provided with a friendly user interface, operation of financial staff and related authorized personnel is facilitated, and meanwhile efficient collaborative operation among the modules is ensured through internal architecture design; according to the method level, from accurate and automatic data collection, collected data are analyzed in real time by using an intelligent algorithm, key information is rapidly extracted and classified, and the data are properly stored in corresponding storage areas according to a preset rule.

Description

Technical Field

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

[0002] Finance refers to the management of assets, liabilities, and income of an enterprise, institution, or individual over a certain period, as well as the decision-making on investment and capital operation. Financial activities are the foundation of enterprise operation and have a crucial impact on the operation, development, and profitability of the enterprise. Financial decisions need to follow a series of principles and standards, including financial statement analysis, risk management, cost control, and tax planning, etc.

[0003] With the continuous expansion of enterprise scale and the increasing complexity of business, the amount of financial data has grown explosively. When facing a large amount of financial data, the traditional financial data management and storage architecture may have performance bottlenecks, resulting in slow query and access speeds, affecting the timeliness of financial reports and decisions. In many existing financial data management systems, data encryption, permission management, and access control are relatively weak. Especially in the cloud storage and remote access environment, there may be risks of data leakage or illegal access. The traditional financial data management method has been difficult to meet the requirements of efficient, accurate, and secure management. Therefore, we propose a financial data management method and system. Summary of the Invention

[0004] The purpose of the present invention is to provide a financial data management method and system to solve the problems raised in the above background art.

[0005] To achieve the above purpose, a financial data management system includes a data collection module, a data analysis engine module, a classification storage module, a permission management module, a monitoring and alarm module, and a data backup and recovery module;

[0006] The data collection module is responsible for connecting with various financial-related systems within the enterprise and external data sources to obtain original financial data;

[0007] The data analysis engine module is responsible for analyzing the collected financial data, deeply analyzing it using data analysis algorithms and models, and extracting valuable information;

[0008] The classification storage module is used to store the analyzed data into different databases or storage areas according to classification rules, while ensuring the security of the data;

[0009] The permission management module is used to ensure that only authorized users can access and operate financial data, protecting the security and compliance of the data;

[0010] The monitoring and alarm module is used to monitor various operations of the system, ensure the normal operation of the system, and handle abnormal events in a timely manner;

[0011] The data backup and recovery module is used to ensure that financial data can be recovered in a timely manner in case of system failures and prevent data loss.

[0012] Preferably, the data acquisition module includes a data extraction unit and an automated crawler unit;

[0013] The data extraction unit uses optical character recognition algorithms to extract text from scanned documents or pictures and identify the content of invoices, checks, and other paper documents;

[0014] The automated crawler unit uses distributed crawler algorithms to extract financial data on websites. The crawler performs intelligent matching and content scraping for different web page structures.

[0015] Preferably, the data analysis engine module includes an anomaly detection unit and a predictive analysis unit;

[0016] The anomaly detection unit uses the Isolation Forest algorithm to detect anomalies or fraud behaviors in financial data;

[0017] The predictive analysis unit uses a long short-term memory neural network model for cash flow prediction and risk assessment.

[0018] Preferably, the classification and storage module includes an intelligent classification unit and a distributed storage unit;

[0019] The intelligent classification unit uses deep learning image classification algorithms to automatically classify financial documents in various formats;

[0020] The distributed storage unit uses object storage technology to store financial data in a distributed manner.

[0021] Preferably, the permission management module includes a role-based access control unit and a behavior analysis and permission dynamic adjustment unit;

[0022] The role-based access control unit sets access permissions according to user roles to ensure that personnel in different positions only access specific financial data;

[0023] The behavior analysis and permission dynamic adjustment unit dynamically adjusts permissions based on machine learning algorithms for user behavior analysis.

[0024] Preferably, the monitoring and alarm module includes a real-time monitoring unit and an intelligent alarm unit;

[0025] The real-time monitoring unit uses streaming processing algorithms to process data streams in real time and monitor the system status;

[0026] The intelligent alarm unit uses a Bayesian network for intelligent fault detection and automatically generates alarm information.

[0027] Preferably, the data backup and recovery module includes an incremental backup unit and a disaster recovery unit;

[0028] The incremental backup unit uses an incremental backup algorithm for efficient data backup;

[0029] The disaster recovery unit uses blockchain technology to ensure the immutability of data and highly reliable disaster recovery capabilities.

[0030] A usage method of a financial data management system according to any one of the above includes the following steps:

[0031] Step 1: The system establishes connections with various financial-related systems within the enterprise and external data sources, and obtains financial raw data in real-time or at regular intervals through preset data interfaces and adaptation protocols; performs preliminary format verification and integrity checks on the collected data, eliminates data with obvious errors or missing key information, and marks abnormal data;

[0032] Step 2: The qualified financial data collected is transmitted to the analysis engine, which has a variety of data analysis algorithms and models built-in; uses these algorithms and models to deeply analyze the financial data; assigns corresponding tags to the data according to the analysis results;

[0033] Step 3: According to the pre-set data classification rules and the tags assigned during the data analysis phase, the financial data is stored in different databases or storage areas respectively; during the storage process, encryption technology is used to encrypt sensitive data, and a data index is established at the same time;

[0034] Step 4: The system administrator sets corresponding data access and operation permissions for different user roles according to factors such as the organizational structure and job functions within the enterprise, and the scope of permissions covers operations such as viewing, modifying, and deleting data; when a user logs in to the system and attempts to access financial data, the system will strictly verify their permissions and only allow corresponding operations within the scope of permissions, and issue an alarm and record a log in a timely manner for unauthorized access behavior.

[0035] Compared with the prior art, the beneficial effects of the present invention are:

[0036] 1. In terms of the system of the present invention, it has a friendly user interface, which is convenient for financial personnel and relevant authorized personnel to operate. At the same time, the internal architecture design ensures the efficient collaborative operation between modules, covering core components such as a data collection module, an analysis engine, a storage unit, and a permission management module. Each component closely cooperates to jointly achieve the all-round management of financial data.

[0037] 2. At the method level of the present invention, starting from precise and automated data collection, intelligent algorithms are used to analyze the collected data in real time, quickly extract key information and classify it, store the data properly in the corresponding storage areas according to preset rules, and strictly control the access and operation of the data according to the permission settings throughout the process to prevent data leakage and illegal use, thereby overall improving the quality and efficiency of financial data management and providing strong support for the financial management decision-making of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a flowchart of the method module of the present invention;

[0039] Figure 2 It is a diagram of the overall system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0041] Please refer to Figure 1 , the present invention provides a technical solution: a financial data management system, including a data collection module, a data analysis engine module, a classification storage module, a permission management module, a monitoring and alarm module, and a data backup and recovery module; the data collection module is responsible for connecting to various financial-related systems within the enterprise and external data sources to obtain financial raw data.

[0042] It should be noted that the specific solution of the data collection module is as follows: develop a general data interface adaptation layer that can be compatible with the data output formats of mainstream financial software and related business systems on the market, and adopt standardized data transmission protocols such as XML and JSON; configure a timing task scheduler to flexibly set the time interval of data collection, and real-time collection can be achieved for key financial data. At the same time, set up a data caching mechanism to prevent data loss caused by network fluctuations and other reasons, and ensure the integrity and reliability of the collection process; deploy a data quality detection program at the data collection end to immediately verify the collected data based on the predefined financial data format specifications and required field requirements.

[0043] The data analysis engine module is responsible for analyzing the collected financial data, and applying data analysis algorithms and models for in-depth analysis to extract valuable information.

[0044] It should be noted that the specific solution for the data analysis engine module is as follows: select an open-source data analysis framework (such as Apache Spark, etc.) and combine it with a self-developed financial data analysis algorithm library to build a powerful analysis engine; for different analysis requirements, flexibly call the corresponding algorithm combinations through configuration files; establish a data model training mechanism, and use historical financial data to regularly train and optimize the anomaly detection model.

[0045] The classification storage module is used to store the analyzed data into different databases or storage areas according to classification rules, while ensuring the security of the data.

[0046] It should be noted that the specific solution for the classification storage module is as follows: adopt a distributed data architecture (such as Hadoop Distributed File System, etc.) to implement the storage of financial data, divide different data storage nodes according to factors such as data category and importance, and connect each node through a high-speed network to ensure the high efficiency of data reading and writing; use data encryption technology (such as AES symmetric encryption algorithm, etc.) to encrypt sensitive financial data (such as bank account numbers, employee salary information, etc.) stored, and the encryption key is stored by a dedicated key management system, strictly controlling the access and usage rights of the key to ensure the confidentiality of the data; develop an intelligent data storage management system that can automatically adjust the storage location of data in the storage system according to the update frequency and access popularity of the data, optimize the storage resource allocation, and improve the storage and retrieval efficiency of the data.

[0047] The permission management module is used to ensure that only authorized users can access and operate financial data, protecting the security and compliance of the data.

[0048] It should be noted that the specific solution for the permission management module is as follows: build a role-based access control (RBAC) model, classify the internal users of the enterprise according to their job roles, and define a detailed permission list for each role; the permission configuration is operated through a visual management interface, and the administrator can conveniently add, modify, and delete the

[0049] permissions of roles. At the same time, the system automatically records the permission change logs; in the user login authentication process, adopt a multi-factor authentication method (such as password + dynamic verification code / fingerprint recognition, etc.) to enhance the security of user authentication, and during the process of users operating on the data, monitor the operation behavior in real time, block and alarm abnormal operations in a timely manner.

[0050] The monitoring and alarm module is used to monitor various operations of the system to ensure the normal operation of the system and handle abnormal events in a timely manner.

[0051] It should be noted that the specific solution of the monitoring and alarm module is as follows: real-time monitoring of all links of data collection, analysis, storage and permission management; timely sending alarm notifications for abnormal situations occurring in the system; generating operation logs to record all important operations.

[0052] The data backup and recovery module is used to ensure that financial data can be restored in a timely manner in case of system failures and prevent data loss.

[0053] It should be noted that the specific solution of the data backup and recovery module is as follows: regularly and automatically backing up financial data, including original data, analysis results and tag data; being able to quickly restore data in case of system failures.

[0054] The data collection module includes a data extraction unit and an automated crawler unit; the data extraction unit uses the optical character recognition algorithm (OCR) to extract text from scanned documents or pictures and recognize the content of invoices, checks and other paper documents; the automated crawler unit uses the distributed crawler algorithm to extract financial data on websites, and the crawler performs intelligent matching and content scraping for different web page structures.

[0055] It should be noted that the data extraction unit can convert the handwritten or printed text in the image into structured data through the OCR algorithm to ensure the digitization of financial documents, and the automated crawler unit can use the crawler to automatically obtain real-time data such as financial reports and bank account dynamics on the web page and compare them with local data to detect omissions.

[0056] The data analysis engine module includes an anomaly detection unit and a predictive analysis unit; the anomaly detection unit uses the isolation forest algorithm to detect anomalies or fraud behaviors in financial data; the predictive analysis unit uses the long short-term memory (LSTM) neural network model for cash flow prediction and risk assessment.

[0057] It should be noted that the anomaly detection unit uses the isolation forest algorithm to detect anomalies or fraud behaviors in financial data. This algorithm is based on the decision tree model and can effectively identify abnormal data that is significantly different from most data points to help detect potential financial fraud activities. The predictive analysis unit processes historical financial data through LSTM to predict future cash flow trends and assist financial managers in making good fund arrangements.

[0058] The classification storage module includes an intelligent classification unit and a distributed storage unit; the intelligent classification unit uses the deep learning image classification algorithm to automatically classify financial documents in various formats; the distributed storage unit uses object storage technology to store financial data distributively.

[0059] It should be noted that the intelligent classification unit automatically classifies the file content through image recognition technology, and establishes corresponding indexes to reduce manual intervention. The distributed storage unit divides the data into multiple independent storage blocks and uses distributed computing and storage technologies to improve data accessibility and fault tolerance.

[0060] The permission management module includes a role-based access control unit and a behavior analysis and permission dynamic adjustment unit; the role-based access control unit sets access permissions according to the user role to ensure that personnel in different positions only access specific financial data; the behavior analysis and permission dynamic adjustment unit dynamically adjusts permissions based on the machine learning algorithm of user behavior analysis.

[0061] It should be noted that in the role-based access control unit (RBAC), the system dynamically assigns permissions based on the user identity and job role, executes a flexible permission control policy, and the behavior analysis and permission dynamic adjustment unit adjusts the permission policy by analyzing user behavior (such as access frequency, frequently accessed modules, etc.) to improve security and reduce potential risks.

[0062] The monitoring and alarm module includes a real-time monitoring unit and an intelligent alarm unit; the real-time monitoring unit uses a streaming processing algorithm to process the data stream in real time and monitor the system status; the intelligent alarm unit uses a Bayesian network for intelligent fault detection and automatically generates alarm information.

[0063] It should be noted that the real-time monitoring unit obtains the financial data stream, system status, and error logs in real time through streaming processing, monitors the system load and data quality, and the intelligent alarm unit uses the Bayesian probability inference method to identify abnormal patterns and issue alarm signals to ensure that problems are discovered and repaired in a timely manner.

[0064] The data backup and recovery module includes an incremental backup unit and a disaster recovery unit; the incremental backup unit uses an incremental backup algorithm for efficient data backup; the disaster recovery unit uses blockchain technology to ensure data immutability and highly reliable disaster recovery capabilities.

[0065] It should be noted that the incremental backup unit only backs up the data that has changed since the last backup, saving storage space and improving backup efficiency. The disaster recovery unit uses the technology of the distributed ledger to ensure data integrity and quickly recover lost data in the event of a system crash.

[0066] According to the usage method of a financial data management system described in any one of the above, the method includes the following steps:

[0067] Step 1: The system establishes connections with various financial-related systems within the enterprise (such as financial software, reimbursement systems, invoicing systems, etc.) and external data sources (such as bank interfaces, etc.). Through preset data interfaces and adaptation protocols, it obtains financial raw data in real-time or at regular intervals; conducts preliminary format verification and integrity checks on the collected data, eliminates data with obvious errors or missing key information, and marks abnormal data;

[0068] Step 2: The qualified financial data collected is transmitted to the analysis engine. The analysis engine is built-in with various data analysis algorithms and models, such as data comparison algorithms, trend analysis models, anomaly detection models, etc.; uses these algorithms and models to deeply analyze the financial data. For example, compare the expenditure of the same type of expenses in different periods to detect abnormal fluctuations, analyze the revenue trend to provide reference for business decisions, and at the same time detect possible financial risk points, such as abnormal large-scale fund flows, etc.; assign corresponding labels to the data according to the analysis results for subsequent classified storage and quick retrieval;

[0069] Step 3: According to the pre-set data classification rules and the labels assigned in the data analysis stage, the financial data is stored in different databases or storage areas respectively. For example, store data such as income, cost, and expenses according to accounting subjects, or collect and store relevant financial data according to business segments; during the storage process, use encryption technology to encrypt sensitive data to ensure the security of the data in the storage state, and at the same time establish a data index for convenient subsequent quick query and call;

[0070] Step 4: The system administrator sets corresponding data access and operation permissions for different user roles (such as financial managers, accountants, auditors, etc.) according to factors such as the organizational structure and job functions within the enterprise. The scope of permissions covers operations such as viewing, modifying, and deleting data; when a user logs in to the system and attempts to access financial data, the system will strictly verify their permissions and only allow corresponding operations within the scope of permissions. An alarm will be issued in a timely manner for unauthorized access behavior and the log will be recorded to ensure the safe and compliant use of financial data.

[0071] In summary, the financial data management method and system of the present invention innovatively integrates multiple technologies, systematically optimizing from the source of data generation to the final storage and usage links. In terms of the system, it has a friendly user interface, facilitating the operation of financial personnel and relevant authorized personnel, while the internal architecture design ensures the efficient collaborative operation among modules, covering core components such as a data collection module, an analysis engine, a storage unit, and a permission management module. These components work closely together to achieve the all-round management of financial data. At the method level, starting from accurate and automated data collection, intelligent algorithms are used to analyze the collected data in real time, quickly extract key information and classify it, and store the data properly in the corresponding storage area according to preset rules. Throughout the process, data access and operations are strictly controlled according to the permission settings to prevent data leakage and illegal use, overall improving the quality and efficiency of financial data management and providing strong support for enterprise financial management decisions.

[0072] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A financial data management system, characterized in that, It includes a data collection module, a data analysis engine module, a classification storage module, a permission management module, a monitoring and alarm module, and a data backup and recovery module; The data collection module is responsible for connecting to various internal enterprise financial-related systems and external data sources to obtain raw financial data; The data analysis engine module is responsible for analyzing the collected financial data, applying data analysis algorithms and models for in-depth analysis, and extracting valuable information; The classification storage module is used to store the analyzed data into different databases or storage areas according to classification rules, while ensuring data security; The permission management module is used to ensure that only authorized users can access and operate financial data, protecting data security and compliance; The monitoring and alarm module is used to monitor various operations of the system, ensure the normal operation of the system, and handle abnormal events in a timely manner; The data backup and recovery module is used to ensure that financial data can be recovered in a timely manner in case of system failures, preventing data loss.

2. A financial data management system according to claim 1, characterized in that, The data collection module includes a data extraction unit and an automated crawler unit; The data extraction unit uses optical character recognition algorithms to extract text from scanned documents or pictures, and identify the content of invoices, checks, and other paper documents; The automated crawler unit uses distributed crawler algorithms to extract financial data on websites. The crawler performs intelligent matching and content scraping for different web page structures.

3. A financial data management system according to claim 1, characterized in that, The data analysis engine module includes an anomaly detection unit and a predictive analysis unit; The anomaly detection unit uses the Isolation Forest algorithm to detect anomalies or fraud behaviors in financial data; The predictive analysis unit uses a Long Short-Term Memory neural network model for cash flow prediction and risk assessment.

4. A financial data management system according to claim 1, characterized in that, The classification storage module includes an intelligent classification unit and a distributed storage unit; The intelligent classification unit uses deep learning image classification algorithms to automatically classify various formats of financial documents; The distributed storage unit uses object storage technology to store financial data in a distributed manner.

5. A financial data management system according to claim 1, wherein, The permission management module includes a role-based access control unit and a behavior analysis and permission dynamic adjustment unit; The role-based access control unit sets access permissions according to user roles to ensure that personnel in different positions only access specific financial data; The behavior analysis and permission dynamic adjustment unit dynamically adjusts permissions based on machine learning algorithms for user behavior analysis.

6. A financial data management system according to claim 1, characterized in that, The monitoring and alarm module includes a real-time monitoring unit and an intelligent alarm unit; The real-time monitoring unit uses streaming processing algorithms to process data streams in real time and monitor the system status; The intelligent alarm unit uses Bayesian networks for intelligent fault detection and automatically generates alarm information.

7. A financial data management system according to claim 1, characterized in that, The data backup and recovery module includes an incremental backup unit and a disaster recovery unit; The incremental backup unit uses incremental backup algorithms for efficient data backup; The disaster recovery unit uses blockchain technology to ensure data immutability and highly reliable disaster recovery capabilities.

8. A method for using a financial data management system according to any one of claims 1-7, characterized in that It includes the following steps: Step 1: The system establishes connections with various financial-related systems within the enterprise and external data sources, and obtains raw financial data in real-time or at regular intervals through preset data interfaces and adaptation protocols; conducts preliminary format verification and integrity checks on the collected data, eliminates data with obvious errors or missing key information, and marks abnormal data; Step 2: The qualified financial data collected is transmitted to the analysis engine, which is built-in with a variety of data analysis algorithms and models; uses these algorithms and models to deeply analyze the financial data; assigns corresponding tags to the data according to the analysis results; Step 3: According to the pre-set data classification rules and the tags assigned during the data analysis stage, the financial data is stored in different databases or storage areas respectively; during the storage process, encryption technology is used to encrypt sensitive data, and at the same time, data indexes are established; Step 4: The system administrator sets corresponding data access and operation permissions for different user roles according to the organizational structure and job functions within the enterprise, and the scope of permissions covers operations such as viewing, modifying, and deleting data; when a user logs in to the system and attempts to access financial data, the system will strictly verify their permissions and only allow corresponding operations within the scope of permissions, and issue an alarm and record a log in a timely manner for unauthorized access behavior.

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