General bypass account checking and early warning system based on big data

Abstract

The invention provides a general bypass reconciliation and early warning system based on big data, which belongs to the technical field of information, adopts a layered architecture design and comprises a data acquisition layer, a data processing layer, an intelligent reconciliation layer, an early warning decision-making layer and a user interaction layer. All the layers carry out efficient data exchange and instruction transmission through standardized interfaces, it is ensured that all function modules of the system can work cooperatively, and automatic account checking and abnormal early warning of cross-system and cross-platform transaction data are achieved.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a general bypass reconciliation and early warning system based on big data. Background Art

[0002] In the current business environment, the number of bypass transactions is increasing, which brings great challenges to reconciliation work. Traditional reconciliation methods rely on manual operations, which are time-consuming and labor-intensive and difficult to cope with the needs of efficient and accurate reconciliation under large amounts of data. At the same time, traditional methods lack an effective early warning mechanism for potential risks in bypass transactions. Therefore, it is urgent to develop a universal bypass reconciliation and early warning system based on big data to improve reconciliation efficiency and accuracy and enhance risk identification and prevention capabilities. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides a general bypass reconciliation and early warning system based on big data, which realizes comprehensive and automated reconciliation of bypass transactions and provides intelligent risk early warning function.

[0004] The technical solution of the present invention is:

[0005] A general bypass reconciliation and early warning system based on big data, comprising:

[0006] Data integration and preprocessing module: responsible for collecting bypass transaction data from various business systems, and performing cleaning, conversion and standardization to ensure data accuracy and consistency.

[0007] Big data processing and analysis module: Utilize big data processing technology and algorithms to conduct in-depth analysis of pre-processed data and identify differences and anomalies between bypass transactions and mainstream transactions.

[0008] Intelligent rule engine module: Based on business rules and logic, an intelligent rule engine is built to automatically compare the data of bypass transactions and mainstream transactions, generate reconciliation result reports, and support custom rules and threshold settings.

[0009] Risk warning and monitoring module: Combining big data analysis and machine learning technology, build a risk warning model to monitor potential risks in bypass transactions in real time and issue warning signals.

[0010] User interaction and visualization module: provides an interactive interface and visualization function to display reconciliation results and risk warning information, so that users can quickly understand business status and risk situation.

[0011] Furthermore,

[0012] It also includes security and privacy protection mechanisms: using encryption technology and data desensitization methods to ensure the security and privacy of transaction data.

[0013] Going further,

[0014] The data integration and preprocessing module adds support for more data sources, including unstructured data and real-time data streams, while optimizing data cleaning and conversion algorithms to improve the efficiency and accuracy of data processing.

[0015] The big data processing and analysis module introduces big data processing frameworks and algorithms to improve the speed and efficiency of data processing; at the same time, it increases the ability to identify and analyze complex transaction patterns.

[0016] The intelligent rule engine module increases the flexibility and scalability of the rule engine, allowing users to define and modify reconciliation rules; at the same time, it introduces machine learning algorithms to enable the rule engine to automatically optimize and adjust rules based on historical data.

[0017] The risk warning and monitoring module adds more risk warning models and indicators, while improving the accuracy and timeliness of warnings to increase the ability to predict and evaluate potential risks.

[0018] The user interaction and visualization experience module optimizes the user interface design, improves the system's usability and accessibility, and adds more visualization display methods and interactive functions.

[0019] Furthermore,

[0020] The products and components used mainly include:

[0021] Financial management system: This system can be directly integrated into the company's financial management software as its core reconciliation and risk management module to enhance the intelligence level of financial management.

[0022] ERP (Enterprise Resource Planning) System: This reconciliation and early warning system is embedded in the ERP system to achieve automatic reconciliation and abnormal monitoring of data in multiple links such as supply chain, production, and sales, thereby enhancing the transparency and efficiency of the company's overall operations.

[0023] Payment and settlement platform: For companies such as finance and e-commerce that involve large amounts of capital flows, this system can be used as part of the payment and settlement platform to ensure the accuracy of transaction data and provide instant warnings of potential capital risks.

[0024] Data analysis platform: As an independent data analysis tool, it provides powerful reconciliation and early warning functions for the data analysis department of the enterprise, helping enterprises to extract value from massive data and optimize the decision-making process.

[0025] For the company, this invention has the following value:

[0026] Improve operational efficiency: Automated reconciliation and early warning processes reduce manual intervention, significantly improve the company's financial processing efficiency and operational speed, and reduce labor costs.

[0027] Enhanced risk management capabilities: Through real-time monitoring and early warning mechanisms, enterprises can promptly detect and respond to potential financial risks, data inconsistencies and other issues to ensure the financial security and business continuity of the enterprise.

[0028] Optimized decision support: Based on big data analysis and machine learning technology, the system can provide enterprises with in-depth data insights and predictive analysis, providing strong support for their strategic decision-making and operational optimization.

[0029] Improve customer satisfaction: By improving the accuracy and efficiency of reconciliation, reducing customer complaints and disputes caused by data errors or delays, and improving customer satisfaction and loyalty.

[0030] Enhance market competitiveness: As one of the core competitiveness of the enterprise, the system can improve the overall level of the enterprise in financial management, risk management, data analysis, etc., and enhance the competitiveness and influence of the enterprise in the market.

[0031] The present invention can be widely used in finance, e-commerce, logistics, supply chain and other fields to meet the reconciliation and early warning needs of cross-system and cross-platform transaction data of enterprises. The system has the following significant advantages: first, it improves the efficiency and accuracy of reconciliation and reduces manual intervention and errors; second, it enhances risk management capabilities, timely discovers and warns of potential risks; third, it optimizes decision support and provides enterprises with valuable data insights and predictive analysis; fourth, it improves customer satisfaction and loyalty, and reduces customer complaints and disputes by improving reconciliation efficiency and accuracy; fifth, it enhances market competitiveness, and as one of the core competitiveness of enterprises, it improves the overall operation level and market competitiveness of enterprises.

[0032] The present invention enhances data integration and preprocessing capabilities: The present invention increases support for more data sources, including unstructured data and real-time data streams, while optimizing data cleaning and conversion algorithms to improve the efficiency and accuracy of data processing.

[0033] Optimize big data processing and analysis modules: This invention introduces more advanced big data processing frameworks and algorithms, such as distributed computing and parallel processing, to improve the speed and efficiency of data processing. At the same time, it increases the ability to identify and analyze complex transaction patterns.

[0034] Upgrade of intelligent rule engine: This invention increases the flexibility and scalability of the rule engine, allowing users to more easily define and modify reconciliation rules. At the same time, the introduction of machine learning algorithms enables the rule engine to automatically optimize and adjust rules based on historical data.

[0035] Enhanced risk warning and monitoring functions: The present invention adds more risk warning models and indicators, such as abnormal transaction frequency, abnormal transaction amount, etc., while improving the accuracy and timeliness of warnings. It also increases the ability to predict and evaluate potential risks.

[0036] Improve user interaction and visualization experience: This invention optimizes the user interface design, improves the usability and accessibility of the system, and adds more visualization display methods and interactive functions, such as dynamic charts, interactive reports, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of the workflow of the present invention. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0039] The present invention proposes a general bypass reconciliation and early warning system based on big data, which specifically includes the following technical solutions:

[0040] 1. Data integration and preprocessing module: responsible for collecting bypass transaction data from various business systems, and performing cleaning, conversion and standardization to ensure data accuracy and consistency.

[0041] 2. Big data processing and analysis module: Utilize big data processing technology and algorithms to conduct in-depth analysis of pre-processed data and identify differences and anomalies between side-channel transactions and mainstream transactions.

[0042] 3. Intelligent rule engine module: Based on business rules and logic, build an intelligent rule engine to automatically compare the data of bypass transactions and mainstream transactions, generate reconciliation result reports, and support custom rules and threshold settings.

[0043] 4. Risk warning and monitoring module: Combining big data analysis and machine learning technology, build a risk warning model to monitor the potential risks in bypass transactions in real time and issue warning signals.

[0044] 5. User interaction and visualization module: provides a friendly interactive interface and visualization function to display reconciliation results, risk warnings and other information, so that users can quickly understand business status and risk situations.

[0045] 6. Security and privacy protection mechanism: Use advanced encryption technology and data desensitization methods to ensure the security and privacy of transaction data.

[0046] The present invention adopts a layered architecture design, including a data collection layer, a data processing layer, an intelligent reconciliation layer, an early warning decision layer, and a user interaction layer. Each layer uses a standardized interface to efficiently exchange data and transmit instructions, ensuring that the functional modules of the system can work together to achieve automatic reconciliation and abnormal warning of cross-system and cross-platform transaction data.

[0047] Patented AI algorithms and models: The patented AI algorithms and models of this invention are the core of the system's intelligent reconciliation and early warning functions. The reconciliation algorithm combines traditional rule matching with advanced machine learning technology, which can automatically identify and process complex reconciliation logic, improving the accuracy and efficiency of reconciliation. The early warning model is based on big data analysis technology to build a model library containing a variety of early warning scenarios. By monitoring data changes in real time and matching them with the rules in the model library, potential risk points can be discovered and warned in a timely manner. In addition, the system also has self-learning capabilities, which can continuously optimize algorithms and models to improve the intelligence level of the system.

[0048] The data processing and analysis method of the present invention adopts a distributed computing framework and big data storage technology to ensure that the system can efficiently and accurately process massive transaction data. During the data processing process, the system will perform pre-processing operations such as cleaning, conversion, and standardization on the data to improve the data quality. In the data analysis stage, the system uses a variety of analysis methods such as data mining, correlation analysis, and trend prediction to deeply explore the value behind the data and provide strong support for the company's decision-making. At the same time, the system also supports data visualization functions to help users intuitively understand the results of data analysis.

[0049] The present invention fully considers the requirements of security and reliability in the design and implementation process. The system adopts security measures such as encrypted transmission and permission control to ensure the security of data transmission and storage. At the same time, the system has mechanisms such as disaster recovery backup and load balancing to improve the stability and reliability of the system. During the data processing and analysis process, the system will also perform real-time data verification and anomaly detection to ensure the accuracy and integrity of the data. In addition, the system also has log recording and auditing functions to facilitate tracing and troubleshooting.

[0050] The present invention can significantly improve the efficiency and accuracy of reconciliation and reduce the cost of reconciliation. At the same time, it can enhance the ability to identify and prevent risks, and solve the problems of difficult reconciliation of bypass transactions and untimely risk warning in the current business system. By integrating technologies such as big data processing, intelligent analysis, and rule engines, the automated reconciliation and intelligent warning of bypass transactions are realized, the efficiency and accuracy of reconciliation are improved, the risk prevention and control capabilities are enhanced, and the business security and stability are improved. The present invention is applicable to multiple industries such as finance, e-commerce, and logistics.

[0051] The above description is only a preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, and is not used to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A general bypass reconciliation and early warning system based on big data, characterized in that: include: Data integration and preprocessing module: responsible for collecting bypass transaction data from various business systems, and performing cleaning, conversion and standardization to ensure data accuracy and consistency; Big data processing and analysis module: Use big data processing technology and algorithms to conduct in-depth analysis of pre-processed data to identify differences and anomalies between side-channel transactions and mainstream transactions; Intelligent rule engine module: Based on business rules and logic, build an intelligent rule engine to automatically compare the data of bypass transactions and mainstream transactions, generate reconciliation result reports, and support custom rules and threshold settings; Risk warning and monitoring module: Combining big data analysis and machine learning technology, build a risk warning model to monitor the potential risks in bypass transactions in real time and issue warning signals; User interaction and visualization module: provides an interactive interface and visualization function to display reconciliation results and risk warning information, so that users can quickly understand business status and risk situation.

2. The system according to claim 1, characterized in that It also includes security and privacy protection mechanisms: using encryption technology and data desensitization methods to ensure the security and privacy of transaction data.

3. The system according to claim 1, characterized in that The data integration and preprocessing module adds support for more data sources, including unstructured data and real-time data streams, while optimizing data cleaning and conversion algorithms to improve the efficiency and accuracy of data processing.

4. The system according to claim 1, characterized in that The big data processing and analysis module introduces big data processing frameworks and algorithms to improve the speed and efficiency of data processing; at the same time, it increases the ability to identify and analyze complex transaction patterns.

5. The system according to claim 1, characterized in that The intelligent rule engine module increases the flexibility and scalability of the rule engine, allowing users to define and modify reconciliation rules; at the same time, it introduces machine learning algorithms to enable the rule engine to automatically optimize and adjust rules based on historical data.

6. The system according to claim 1, characterized in that The risk warning and monitoring module adds more risk warning models and indicators, while improving the accuracy and timeliness of warnings to increase the ability to predict and evaluate potential risks.

7. The system according to claim 1, characterized in that The user interaction and visualization experience module optimizes the user interface design, improves the system's usability and accessibility, and adds more visualization display methods and interactive functions.

8. The system according to claim 1, characterized in that Applicable Financial management system, directly integrated into the enterprise's financial management software as its core reconciliation and risk management module; ERP system: embed this reconciliation and early warning system in the ERP system to realize automatic reconciliation and abnormal monitoring of data in each link; Payment and settlement platform: As part of the payment and settlement platform, it ensures the accuracy of transaction data and instantly warns of potential financial risks; Data analysis platform: As an independent data analysis tool, it provides reconciliation and early warning functions, helping enterprises to extract value from massive data and optimize the decision-making process.

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