Financial information processing system and method

Abstract

The invention belongs to the technical field of financial information processing, and discloses a financial information processing system and method.The financial information processing system is characterized in that a user side is connected with a verification module and an intelligent customer service module, the verification module is connected with an encryption transmission module, and the encryption transmission module is connected with a processing unit; the processing unit comprises a bank management module, a financial transaction module and a transaction processing module, the processing unit is connected with the payment gateway, the payment gateway is connected with the transaction unit, the transaction unit comprises a bank, a UnionPay and a third party, and the transaction processing module is further connected with a storage unit. A data storage service module and an abnormal transaction monitoring module are integrated in the storage unit; the verification module, the processing unit, the data storage service module, the abnormal transaction monitoring module, the payment gateway, the transaction unit and the intelligent customer service module are all connected with the cloud management platform. According to the invention, the security, user experience and stability of the system are remarkably improved, and meanwhile, powerful data mining and risk management capabilities are provided for financial institutions.

Description

Technical Field

[0001] The present invention belongs to the technical field of financial information processing, and in particular relates to a financial information processing system and method. Background Art

[0002] With the rapid advancement of the internet, its commercial potential is being continuously tapped. Internet finance, as a key component of this trend, has attracted the attention of numerous companies. However, selling financial services online still faces several challenges. Security and privacy protection are of primary concern to consumers. Since financial transactions involve large amounts of sensitive personal information and financial transactions, ensuring the security and reliability of the transaction process and preventing information leaks and fraud have become key issues that internet companies must address.

[0003] To sum up, the rapid development of Internet finance has provided consumers with more convenient and diverse financial service options, but it has also put higher demands on financial enterprises and Internet companies. While ensuring transaction convenience and efficiency, strengthening security and privacy protection will become an important direction for the future development of Internet finance. Summary of the Invention

[0004] The purpose of the present invention is to provide a financial information processing system and method to solve the problems existing in the above-mentioned prior art.

[0005] On the one hand, to achieve the above-mentioned purpose, the present invention provides a financial information processing system, including a user terminal and a cloud management platform, the user terminal is bidirectionally connected to a verification module and an intelligent customer service module, the verification module is connected to an encryption transmission module, the encryption transmission module is connected to a processing unit, the processing unit includes a bank management module, a financial transaction module and a transaction processing module, the processing unit is connected to a payment gateway, the payment gateway is connected to a transaction unit, the transaction unit includes a bank, UnionPay and a third party, the transaction processing module is also connected to a storage unit, the storage unit is integrated with a data storage service module and an abnormal transaction monitoring module; the verification module, processing unit, data storage service module, abnormal transaction monitoring module, payment gateway, transaction unit and intelligent customer service module are all connected to the cloud management platform.

[0006] Optionally, a user interaction module and a user feedback module are integrated in the user terminal, and both the user interaction module and the user feedback module are connected to the cloud management platform.

[0007] Optionally, the cloud management platform includes a main control module and an abnormal transaction monitoring model training module, a cloud storage module and a communication module connected to the main control module, and the communication module includes one or more of a LoRa module, a 2.5G communication module, a 3G communication module, a 4G communication module and a 5G communication module.

[0008] Optionally, the verification module is connected to the main control module of the cloud management platform through a verification management module.

[0009] Optionally, the verification module includes a text message platform submodule, a CA center, and a biometric information verification submodule.

[0010] Optionally, the verification management module includes a security management module, a certificate management module and an SMS gateway.

[0011] Optionally, the encrypted transmission module includes an encryption layer, a transmission layer and a verification layer connected in sequence, the encryption layer is also connected to the verification module and the cloud management platform, and the output end of the verification layer is connected to the cloud management platform and the processing unit.

[0012] Optionally, the intelligent customer service module includes an information processing submodule and a personalized recommendation submodule.

[0013] On the other hand, to achieve the above-mentioned purpose, the present invention provides a financial information processing method, which is applied to the above-mentioned financial information processing system, comprising:

[0014] The user logs in to the client and interacts with the intelligent customer service module to initiate a transaction request. The verification module performs multi-factor authentication on the user. Once the authentication is passed, the user information and transaction request are transmitted to the processing module through the encrypted transmission module. After being processed by the transaction processing module, the transaction is executed through the transaction unit.

[0015] The data storage service module is used to store real-time transaction data, verification data and user information during the transaction process, and the stored data is input into the abnormal transaction monitoring model in the abnormal transaction monitoring module for classification and prediction, and the abnormal detection results are input, and an early warning notification is sent according to the abnormal detection results; wherein, the abnormal transaction monitoring model is constructed based on a deep learning model.

[0016] Optionally, the training process of the abnormal transaction monitoring model specifically includes:

[0017] Acquire training data, wherein the training data includes transaction training data and corresponding anomaly detection labels;

[0018] In the abnormal transaction monitoring model training module, an initial abnormal transaction monitoring model is constructed based on a long short-term memory network. The initial abnormal transaction monitoring model includes an input layer, an LSTM layer, and an output layer connected in sequence. The output layer is input into the initial abnormal transaction monitoring model, and the long-term dependency in the time series is captured by LSTM units (e.g., 50 units) in the LSTM layer. The probability that the current transaction is abnormal is output through the output layer to obtain an initial prediction result. The training is performed with the goal of minimizing the loss between the initial prediction result and the anomaly detection label corresponding to the transaction training data, thereby obtaining a trained abnormal transaction monitoring model.

[0019] The trained abnormal transaction monitoring model is deployed to the abnormal transaction monitoring module to process transaction data in real time.

[0020] The technical effects of the present invention are:

[0021] This invention significantly enhances system security, user experience, and stability through technologies such as multi-factor authentication, encrypted transmission, personalized recommendations, intelligent customer service, and real-time monitoring. It also provides financial institutions with powerful data mining and risk management capabilities. The system's modular design and cloud service support ensure its scalability and adaptability to future business development needs. Ultimately, the system not only optimizes the user experience but also brings significant business growth and operational efficiency improvements to financial institutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0023] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0024] Figure 1 is a system structure diagram in an embodiment of the present invention;

[0025] Figure 2 This is a flow chart of information processing in an embodiment of the present invention.

[0026] Explanation of numbers: 1. User end; 2. Verification module; 3. Encrypted transmission module; 4. Transaction processing module; 401. Bank management module; 402. Financial transaction module; 403. Transaction processing module; 5. Payment gateway; 6. Transaction unit; 601. Bank; 602. UnionPay; 603. Third-party payment institution; 7. Cloud management platform; 8. Storage module; 801. Data storage service module; 802. Abnormal transaction monitoring module; 9. Verification management module; 10. Intelligent customer service module. DETAILED DESCRIPTION

[0027] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as limiting the present invention, but rather as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0028] It should be understood that the terms described herein are intended only to describe particular embodiments and are not intended to limit the present invention. In addition, for numerical ranges herein, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Each smaller range between any intermediate value within a stated value or stated range and any other stated value or intermediate value within the stated range is also encompassed by the present invention. The upper and lower limits of these smaller ranges may be independently included or excluded within the scope.

[0029] It will be apparent to those skilled in the art that various modifications and variations may be made to the specific embodiments of the present invention without departing from the scope or spirit of the invention. Other embodiments will be apparent to those skilled in the art from the present invention. The present description and examples are intended to be illustrative only.

[0030] The words “include,” “including,” “have,” “contain,” etc. used in this article are open-ended terms, meaning including but not limited to.

[0031] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0032] like Figure 1 - Figure 2As shown, a financial information processing system is provided in this embodiment, including a user terminal 1 and a cloud management platform 7, the user terminal 1 is bidirectionally connected to a verification module 2 and an intelligent customer service module 10, the verification module 2 is connected to an encryption transmission module 3, the encryption transmission module 3 is connected to a processing unit, the processing unit includes a bank management module 401, a financial transaction module 402 and a transaction processing module 4, the processing unit is connected to a payment gateway 5, the payment gateway 5 is connected to a transaction unit 6, the transaction unit 6 includes a bank 601, UnionPay 602 and a third party, the transaction processing module 4 is also connected to a storage unit, the storage unit is integrated with a data storage service module 801 and an abnormal transaction monitoring module 802; the verification module 2, the processing unit, the data storage service module 801, the abnormal transaction monitoring module 802, the payment gateway 5, the transaction unit 6 and the intelligent customer service module 10 are all connected to the cloud management platform 7.

[0033] This embodiment significantly enhances system security, user experience, and stability through technical means such as multi-factor authentication, encrypted transmission, personalized recommendations, intelligent customer service, and real-time monitoring. It also provides financial institutions with powerful data mining and risk management capabilities. The system's modular design and cloud service support ensure its scalability and adaptability to future business development needs. Ultimately, the system not only optimizes the user experience but also brings significant business growth and operational efficiency improvements to financial institutions.

[0034] The user terminal 1 in this implementation includes but is not limited to various smart phones, tablet computers, personal computers (PCs), smart wearable devices, self-service terminal devices and Internet of Things devices, etc., to meet the needs of different users and provide support for the stable operation of the system.

[0035] In this implementation, client 1 connects to verification module 2 for user authentication, connects to intelligent customer service for online consultation and feedback, connects to the personalized recommendation module to receive recommendations, and interacts with third-party applications and services through APIs.

[0036] The client 1 in this implementation is extensible to support future functionality additions and device updates, including:

[0037] API interface support: Integrate with third-party applications and services through standardized API interfaces to expand the functionality of the user end 1.

[0038] Modular design: The software architecture of the user terminal 1 adopts a modular design to facilitate the addition and update of subsequent functions.

[0039] Cloud service support: Data synchronization and storage are achieved through the cloud platform, supporting seamless switching between multiple devices.

[0040] The user provides feedback through the user terminal 1, and the user feedback module receives the feedback and transmits it to the processing module and the cloud management platform 7. The cloud management platform 7 optimizes the system according to the feedback information.

[0041] The verification module 2 is connected to the main control module of the cloud management platform 7 through the verification management module 9. The verification module 2 includes a text message platform module, a CA center and biometric information verification. It ensures the authenticity and security of the transaction through text message verification, CA certificate verification, biometric information verification and other methods.

[0042] The verification management module 9 includes a security management module, a certificate management module and an SMS gateway, and is responsible for the management and maintenance of the verification module 2, as well as the formulation and execution of security policies.

[0043] Building on traditional authentication methods, the system of this embodiment introduces a multi-factor authentication mechanism. In addition to SMS verification and CA certificate verification, it also adds biometric technologies (such as fingerprint recognition and facial recognition) as additional authentication methods. Multi-factor authentication significantly improves authentication security by combining multiple different authentication factors. Even if an attacker obtains a user's password or other single authentication information, they cannot easily bypass multi-factor authentication. This effectively prevents the risk of account theft and transaction tampering, ensuring the security of user funds and information.

[0044] During user registration and login, provide multi-factor authentication options, allowing users to select the appropriate authentication combination based on their needs and device conditions. For example, users can choose a combination of password + SMS verification code + fingerprint recognition for login authentication. Integrate multiple authentication modules in the back-end authentication system, corresponding to different authentication factors. For biometric technology, cooperate with professional biometric service providers and adopt safe and reliable biometric algorithms and equipment to ensure the accuracy and security of biometrics. At the same time, establish a correlation and coordination mechanism for authentication factors. During the authentication process, multiple authentication factors are verified sequentially or simultaneously. Only when all authentication factors are verified will the user be allowed to proceed with subsequent transaction operations. In addition, authentication factor management and update functions should be provided, allowing users to add, delete, or replace authentication factors at any time to adapt to different usage scenarios and security needs.

[0045] The encrypted transmission module 3 includes an encryption layer, a transmission layer, and a verification layer connected in sequence, specifically including:

[0046] The encryption layer is responsible for encrypting user data and generating encrypted data. The encryption layer uses symmetric encryption algorithms (such as AES) and asymmetric encryption algorithms (such as RSA and ECC) for data encryption and key exchange.

[0047] The transport layer is responsible for data transmission and protocol management. It uses TLS / SSL for encrypted data transmission and supports HTTP / 2 to improve transmission efficiency. The transport layer is also responsible for data encapsulation, fragmentation, and retransmission mechanisms to ensure data integrity and reliability.

[0048] Data encapsulation: Encapsulates encrypted data into a standard transmission format, such as JSON, XML, etc. Data encapsulation ensures the format consistency and parsability of data during transmission.

[0049] Data sharding: For large data blocks, data is fragmented into multiple smaller segments for transmission. This improves data transmission reliability and efficiency, reduces the size of individual data packets, and lowers the probability of transmission errors.

[0050] Retransmission mechanism: During data transmission, a retransmission mechanism is established to ensure data integrity and reliability. If a data segment fails to be transmitted, the system will automatically retransmit the segment until the data is fully transmitted.

[0051] The verification layer is connected to the cloud management platform 7 and the processing unit, transmits the encrypted data, and is responsible for generating and verifying the verification value of the data to ensure the integrity of the data. The verification layer uses verification algorithms such as MD5 and SHA-256 to generate the verification value of the data and perform verification at the receiving end.

[0052] Application firewall: Set up in key parts of the system to effectively prevent transaction information leakage and external Trojan attacks, ensuring the overall security of the system.

[0053] The processing module, comprising a bank management module 401, a financial transaction module 402, and a transaction processing module 4, is responsible for processing user transaction requests, managing transaction information, and executing transaction operations. The processing module connects to the verification module 2 to receive authentication results and to the encryption module to receive encrypted data. After processing is complete, it connects to the transaction unit 6 to execute payment and settlement operations. It also connects to the storage module 8 to store transaction information and user data.

[0054] Payment gateway 5: connected to the transaction unit 6, to realize the payment interface connection between the user and the bank 601, UnionPay 602 and third-party payment institution 603.

[0055] Transaction unit 6: includes bank 601, UnionPay 602 and third-party payment institution 603, responsible for processing user payment requests and fund settlement.

[0056] Storage Module 8: Use data integration tools such as Kafka and Flume to achieve real-time collection and integration of multi-source data, providing a comprehensive and accurate data view.

[0057] Introducing data mining and machine learning in storage module 8: Introducing data mining algorithms and machine learning models to improve the intelligence level of data analysis, extract more value from data, and provide support for financial decision-making.

[0058] Automated operation and maintenance: Use automated operation and maintenance tools to achieve automated system deployment, monitoring, and troubleshooting, improving system stability and maintainability.

[0059] Data security and privacy protection: Encrypt and store sensitive data, desensitize it, implement strict access control and authority management strategies, and establish a disaster recovery and backup system to ensure data security and privacy.

[0060] Abnormal transaction monitoring module 802, provided in storage module 8, for using deep learning model to monitor abnormal transaction records in real time;

[0061] The system establishes a real-time monitoring mechanism, analyzing and monitoring transaction data and system logs in real time. By incorporating machine learning algorithms to learn and model user transaction behavior patterns, it can intelligently identify abnormal trading behaviors, such as frequent large-value transactions, remote logins, and unusual trading times. When potential security risks are detected, the system promptly issues warning notifications, prompting users and administrators to take appropriate measures, such as suspending transactions, freezing accounts, and conducting secondary verification. This effectively prevents fraud and attacks, improving the system's security response speed and risk prevention capabilities.

[0062] Implementation Details: A real-time monitoring platform will be deployed within the system to collect and aggregate multi-dimensional data, including transaction data, system logs, and network traffic. Data mining and machine learning techniques will be used to analyze and process this data, constructing user behavior profiles and transaction risk models. The real-time monitoring platform will assess and assess transaction behavior in real time based on pre-set risk rules and model thresholds. When anomalies are detected, the early warning mechanism within the early warning module will be immediately triggered. Early warning notifications can be sent through various channels, such as SMS, email, and app push notifications, ensuring that users and administrators receive timely warning information. Furthermore, a process and feedback mechanism for early warning events will be established to track and process these events, analyze their accuracy and effectiveness, and continuously optimize early warning rules and models to improve their accuracy and practicality.

[0063] The cloud management platform 7 includes a main control module and an abnormal transaction monitoring model training module connected to the main control module, a cloud storage module 8, an early warning module and a communication module. The communication module includes one or more of a LoRa module, a 2.5G communication module, a 3G communication module, a 4G communication module, and a 5G communication module.

[0064] The intelligent customer service module 10 includes an information processing submodule and a personalized recommendation submodule. By collecting and analyzing data such as user transaction history, preferences, and browsing behavior, and leveraging data mining and machine learning algorithms, it provides personalized financial product and service recommendations. For example, based on a user's historical investment preferences, it recommends financial products that meet their risk tolerance and return expectations; or, based on their spending habits, it offers customized credit card offers and installment plans. Personalized recommendations can enhance user satisfaction and loyalty, making users feel that the system cares about and understands them, thereby increasing user usage and stickiness, and promoting the development of financial services.

[0065] Implementation Details: Establish a user data collection and analysis platform to collect various user data in real time, and clean, organize, and store it. Use collaborative filtering, content recommendation, and deep learning algorithms to analyze and mine user data, build user profiles and recommendation models, and dynamically adjust recommendations based on real-time user behavior and contextual information to ensure accuracy and timeliness. Within the user interface, establish a personalized recommendation area to prominently display recommended financial products and services to attract user attention. A user feedback mechanism is also provided, allowing users to evaluate and provide feedback on recommendation results. Based on this feedback, the system continuously optimizes the recommendation algorithms and models to improve the quality and effectiveness of recommendations.

[0066] The introduction of intelligent customer service robots provides users with 24-hour online customer service support. Intelligent customer service can automatically identify and understand user questions and needs. Through natural language processing technology and a pre-set knowledge base, it can quickly and accurately provide users with answers and solutions. Compared with traditional human customer service, intelligent customer service has the advantages of fast response time, long service time, and high processing efficiency. It can effectively alleviate the pressure on human customer service, improve the user service experience, promptly resolve user questions and problems, and enhance user trust and satisfaction with the system.

[0067] This embodiment uses advanced natural language processing technology and machine learning algorithms to develop an intelligent customer service system. A comprehensive knowledge base covering frequently asked questions about financial services, operational guides, product introductions, and other content is established, and the knowledge base is regularly updated and maintained to ensure the accuracy and completeness of the information. Through multiple rounds of dialogue with users, the intelligent customer service system gradually gains a deeper understanding of their problems and needs, providing targeted answers and suggestions. For complex or unsolvable issues, intelligent customer service can automatically transfer to human customer service, achieving human-machine collaboration and ensuring that user issues are effectively resolved. At the same time, an intelligent customer service portal is provided in the user terminal 1 interface, allowing users to initiate inquiries at any time. Furthermore, a performance monitoring and evaluation mechanism for intelligent customer service should be established to monitor and analyze indicators such as intelligent customer service response time, answer accuracy, and user satisfaction, continuously optimizing the performance and effectiveness of intelligent customer service.

[0068] To expand system functionality and integrate with other third-party applications and services, the system provides standardized APIs. These APIs adhere to unified interface specifications and protocols, such as RESTful API and SOAP, and offer excellent compatibility and scalability. Third-party developers can easily access the system through these APIs and access various system functions and services, such as user authentication, transaction query, and payment initiation. This not only enriches the system's functionality but also promotes the development of the financial ecosystem, providing users with more diverse financial services.

[0069] During the API interface design phase, fully investigate industry standards and best practices, and develop unified API interface specifications, including requirements for the interface's URL path, request method, parameter list, and return data format. Use a version management mechanism to perform version control on the API interface. When the interface changes, it can be compatible with calls to the old version of the interface to ensure the stability of third-party applications. At the same time, establish an API document management system to record detailed information such as the function description, usage method, parameter description, etc. of each API interface, and provide online documentation and sample code for third-party developers to access and use. In addition, strict API interface testing should be carried out, including functional testing, performance testing, security testing, etc., to ensure the stability and reliability of the API interface. For the access of third-party applications, establish a strict review and authentication mechanism to ensure the security and legality of the accessed applications and prevent malicious applications from posing a threat to the system.

[0070] The specific implementation process of this system:

[0071] System Initialization and Configuration: Build the cloud management platform 7 and deploy the main control module for overall system management and scheduling. Configure the cloud storage module 8 and select a suitable cloud storage service provider, such as Alibaba Cloud or AWS, to store user data, transaction records, and model parameters. Deploy the abnormal transaction monitoring model training module and prepare historical transaction data for model training. Configure the communication module to support multiple communication protocols (such as LoRa and 2.5G to 5G) to meet the connection requirements of different devices.

[0072] User-side 1 development and deployment: Develop user-side 1 applications for smartphones, tablets, PCs, smart wearable devices, etc., integrate API interface modules, support interaction with third-party applications and services; configure modular design architecture to ensure flexible expansion of subsequent functions.

[0073] Verification module 2 and payment module configuration: Deploy the SMS platform module, CA center and biometric information verification module 2, cooperate with professional biometric service providers to complete the integration of related equipment and algorithms; configure the payment gateway 5 and connect to the interfaces of bank 601, UnionPay 602 and third-party payment institution 603.

[0074] Storage module 8 construction: Use Kafka, Flume and other tools to build a data integration platform to achieve real-time collection and integration of multi-source data, introduce data mining and machine learning modules, and deploy related algorithms and models.

[0075] User registration and authentication: The user fills in personal information through the user terminal 1 and selects a multi-factor authentication method (such as password, SMS verification code, fingerprint recognition, etc.). The system encrypts the user information and stores it in the cloud storage module 8, and generates a unique user ID.

[0076] User login process: The user enters authentication information, and the client 1 sends the information to the verification module 2. The verification module 2 verifies multiple authentication factors in sequence or simultaneously. After the verification is successful, the client 1 establishes a secure connection with the system and enters the main interface.

[0077] Transaction processing and payment: Users initiate transaction requests through client 1, such as transfers, payments, and financial purchases;

[0078] The transaction request data is processed by the encrypted transmission module 3:

[0079] Encryption layer: Data is encrypted using AES symmetric encryption and RSA / ECC asymmetric encryption.

[0080] Transport layer: Data is transmitted through the TLS / SSL protocol and supports the HTTP / 2 protocol to optimize transmission efficiency.

[0081] Verification layer: Use MD5 or SHA-256 to generate verification values to ensure data integrity.

[0082] Transaction Processing: The processing module receives encrypted data and verifies its integrity. Bank management module 401, financial transaction module 402, and transaction processing module 4 work together to process the transaction request. Payment gateway 5 forwards the transaction request to transaction unit 6 (bank 601, UnionPay 602, and third-party payment institution 603), completing the payment and settlement operations. The transaction results are stored in storage module 8 and synchronized with user terminal 1.

[0083] Data security and privacy protection:

[0084] Data encryption storage:

[0085] Sensitive data (such as user information and transaction records) is encrypted and stored, and encryption algorithms such as AES are used to desensitize the data and hide sensitive information.

[0086] Access control and permission management: Implement strict access control policies and assign permissions based on user roles; deploy application firewalls to prevent external attacks and information leakage.

[0087] Disaster recovery and backup: Establish a disaster recovery and backup system, regularly back up data to off-site storage, simulate failure scenarios, and test the recovery capabilities of the backup system.

[0088] Abnormal transaction monitoring and early warning: Deploy a real-time monitoring platform to collect multi-dimensional data such as transaction data, system logs, and network traffic. Use data mining and machine learning technologies to build user behavior profiles and transaction risk models. Leverage deep learning models to analyze transaction data in real time and identify abnormal behavior (such as large transactions and remote logins). When potential risks are detected, the early warning module triggers an alert mechanism, notifying users and administrators via SMS, email, and app push notifications. Administrators then take appropriate measures based on the alert, such as suspending transactions, freezing accounts, and initiating secondary verification.

[0089] Optimize the early warning mechanism: Establish an early warning event processing process and feedback mechanism, analyze the accuracy and effectiveness of early warnings; optimize early warning rules and models based on feedback to improve early warning accuracy.

[0090] Personalized recommendation process:

[0091] Collect user transaction history, preferences, browsing behavior, and other data, store it in cloud storage module 8, and build a recommendation model using collaborative filtering, content recommendation, deep learning, and other algorithms. Dynamically adjust recommendation results based on real-time user behavior and display them in the personalized recommendation area on the user terminal 1. Provide a user feedback mechanism and optimize the recommendation algorithm based on user evaluations.

[0092] Intelligent customer service deployment: Deploy the intelligent customer service module 10, integrate natural language processing technology, and establish a comprehensive knowledge base covering common financial business problems and solutions. Intelligent customer service understands user questions through multiple rounds of dialogue and provides answers or transfers to manual customer service.

[0093] Monitor the performance indicators of intelligent customer service (such as response time and answer accuracy) and optimize its performance.

[0094] System extension and third-party integration:

[0095] API interface development and management: Design standardized API interfaces, follow RESTful API or SOAP protocols, establish an API document management system, provide detailed interface descriptions and sample codes; conduct API interface testing to ensure functionality, performance, and security.

[0096] Third-party application access: Establish a strict review mechanism to verify the security and legality of third-party applications, provide developer support to help third-party developers access the system, and monitor the use of third-party applications to ensure that their impact on the system is minimized.

[0097] System operation, maintenance and optimization:

[0098] Automated operation and maintenance: Deploy automated operation and maintenance tools to achieve automated system deployment, monitoring, and troubleshooting, and regularly check system performance and optimize resource allocation.

[0099] User feedback processing: The user terminal 1 provides a feedback entry, and the user feedback module receives the feedback and transmits it to the cloud management platform 7. The cloud management platform 7 optimizes system functions and user experience based on the feedback information.

[0100] This embodiment builds a secure, efficient, and intelligent financial system through the collaborative work of functional modules such as modular design, multi-factor authentication, encrypted transmission, abnormal transaction monitoring, personalized recommendations, and intelligent customer service. Through standardized API interfaces and automated operation and maintenance tools, the system has good scalability and maintainability, can meet the diverse needs of users, and at the same time ensure the security of users' funds and information.

[0101] This embodiment also provides a financial information processing method: a user logs in to a user terminal 1 and interacts with an intelligent customer service module 10 to initiate a transaction request. The user is authenticated by a verification module 2 using multiple factors. After successful authentication, the user information and transaction request are transmitted to a processing module via an encrypted transmission module 3. After processing by a transaction processing module 4, the transaction is executed by a transaction unit 6.

[0102] The data storage service module 801 is used to store real-time transaction data, verification data and user information during the transaction process, and the stored data is input into the abnormal transaction monitoring model in the abnormal transaction monitoring module 802 for classification and prediction, and the abnormal detection results are input, and an early warning notification is sent according to the abnormal detection results; wherein, the abnormal transaction monitoring model is constructed based on a deep learning model.

[0103] The training process of the abnormal transaction monitoring model specifically includes:

[0104] Acquire training data, wherein the training data includes transaction training data and corresponding anomaly detection labels;

[0105] In the abnormal transaction monitoring model training module, an initial abnormal transaction monitoring model is constructed based on a long short-term memory network. The initial abnormal transaction monitoring model includes an input layer, an LSTM layer, and an output layer connected in sequence. The output layer is input into the initial abnormal transaction monitoring model, and the long-term dependency in the time series is captured by LSTM units (e.g., 50 units) in the LSTM layer. The probability that the current transaction is abnormal is output through the output layer to obtain an initial prediction result. The training is performed with the goal of minimizing the loss between the initial prediction result and the anomaly detection label corresponding to the transaction training data, thereby obtaining a trained abnormal transaction monitoring model.

[0106] The trained abnormal transaction monitoring model is deployed in the abnormal transaction monitoring module 802 to process transaction data in real time.

[0107] The specific implementation process of the financial information processing method of this embodiment includes:

[0108] User login: The user logs in through Client 1 (e.g., mobile app, website). The user enters their username and password and selects a multi-factor authentication method (e.g., SMS verification code, fingerprint recognition, facial recognition, etc.). Client 1 sends the authentication request to Verification Module 2.

[0109] Intelligent customer service interaction: After logging in, users can interact with the intelligent customer service module 10 through the user terminal 1. The intelligent customer service module 10 uses natural language processing (NLP) technology to understand user questions and provide answers or guide user operations. For complex questions, the intelligent customer service module 10 can transfer to human customer service.

[0110] Multi-factor authentication: The verification module 2 receives the authentication information sent by the user terminal 1, and calls the SMS platform module, CA center or biometric information verification module 2 for authentication according to the authentication method selected by the user.

[0111] The verification module 2 feeds back the authentication result (pass or fail) to the user terminal 1.

[0112] If the authentication is successful, the user information and transaction request are transmitted to the processing module through the encryption transmission module 3.

[0113] Encrypted transmission and transaction processing: The encryption module uses symmetric encryption (such as AES) and asymmetric encryption (such as RSA) algorithms to encrypt user information and transaction requests to ensure the security of data during transmission. The encrypted data is transmitted to the processing module via the TLS / SSL protocol.

[0114] Transaction processing: The processing module receives the encrypted data and performs decryption processing. The transaction processing module 4 performs specific transaction operations such as payment, transfer, etc. according to the user's request, and sends the transaction request to the transaction unit 6 for transaction execution.

[0115] Transaction execution and data storage: The transaction unit 6 (such as bank 601, UnionPay 602, third-party payment institution 603) receives the transaction request, executes the transaction operation, and returns the transaction result (success or failure).

[0116] Data storage: The data storage service module 801 stores real-time transaction data, verification data and user information during the transaction process. The stored data includes transaction amount, timestamp, user behavior pattern, geographic location, etc.

[0117] Abnormal transaction monitoring: The storage module 8 inputs real-time transaction data into the abnormal transaction monitoring module 802, and the deep learning model (based on LSTM) in the abnormal transaction monitoring module 802 performs real-time analysis on the transaction data.

[0118] Abnormal transaction monitoring model workflow:

[0119] Input layer: Receives a time series of transaction data, where each time step contains multiple features (such as transaction amount, timestamp, etc.).

[0120] LSTM layer: Capture long-term dependencies in time series through LSTM units (e.g., 50 units).

[0121] Output layer: Outputs the probability that the current transaction is abnormal.

[0122] Anomaly Detection and Warning: Based on a set threshold (e.g., anomaly probability greater than 0.8), the system determines whether a transaction is abnormal. If an abnormal transaction is detected, the system sends an alert notification to the user via SMS, email, or app push notification. The system can also automatically take measures such as suspending transactions, freezing accounts, or requiring users to undergo secondary verification.

[0123] Training process of abnormal transaction monitoring model:

[0124] Get training data:

[0125] The historical transaction data is obtained from the storage module 8, including normal transactions and transactions marked as abnormal.

[0126] Data preprocessing: cleaning data, extracting features, normalization, etc.

[0127] Constructing the initial abnormal transaction monitoring model:

[0128] In the abnormal transaction monitoring model training module, an initial model is built based on the long short-term memory network (LSTM).

[0129] The model architecture includes:

[0130] Input layer: Receives time series of transaction data.

[0131] LSTM layer: Contains 50 LSTM units to capture long-term dependencies in time series.

[0132] Output layer: Outputs the probability that the current transaction is abnormal.

[0133] Model training:

[0134] The training data is fed into the initial model. The LSTM layer captures long-term dependencies in the time series. The output layer outputs the probability that the current transaction is an anomaly. Binary cross entropy is used to calculate the difference between the predicted result and the anomaly detection label. The Adam optimizer adjusts the model parameters to minimize the loss function. The training process continues until the model performance converges. The trained abnormal transaction monitoring model is deployed in the abnormal transaction monitoring module 802. The model processes transaction data in real time, outputs anomaly detection results, and triggers the early warning mechanism based on the anomaly detection results.

[0135] Model optimization and feedback: The model regularly learns from new transaction data and updates its parameters to adapt to new transaction patterns and fraud methods; it uses incremental learning methods to fine-tune the model based on new data.

[0136] Users can provide feedback on early warning results, and the system adjusts model parameters based on user feedback and optimizes early warning rules. User feedback and system operation data are collected to analyze the accuracy and effectiveness of early warnings and further optimize the model.

[0137] This embodiment uses a deep learning model (based on LSTM) for real-time analysis, enabling the system to efficiently identify abnormal trading behavior and trigger alerts. Furthermore, it incorporates user feedback and a continuous learning mechanism to continuously improve the model's accuracy and adaptability. This comprehensive solution provides a strong guarantee for the security and reliability of financial transactions.

[0138] The above description is merely a preferred embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A financial information processing system, comprising a user terminal (1) and a cloud management platform (7), characterized in that: The user terminal (1) is bidirectionally connected to a verification module (2) and an intelligent customer service module (10); the verification module (2) is connected to an encryption transmission module (3); the encryption transmission module (3) is connected to a processing unit; the processing unit includes a bank management module (401), a financial transaction module (402) and a transaction processing module (4); the processing unit is connected to a payment gateway (5); the payment gateway (5) is connected to a transaction unit (6); the transaction unit (6) includes a bank (601), UnionPay (602) and a third-party payment institution (603); the transaction processing module (4) is further connected to a storage unit; the storage unit is integrated with a data storage service module (801) and an abnormal transaction monitoring module (802); the verification module (2), the processing unit, the data storage service module (801), the abnormal transaction monitoring module (802), the payment gateway (5), the transaction unit (6) and the intelligent customer service module (10) are all connected to a cloud management platform (7).

2. A financial information processing system according to claim 1, characterized in that: The user terminal (1) is integrated with a user interaction module and a user feedback module, and both the user interaction module and the user feedback module are connected to the cloud management platform (7).

3. A financial information processing system according to claim 1, characterized in that: The cloud management platform (7) includes a main control module and an abnormal transaction monitoring model training module connected to the main control module, a cloud storage module (8) and a communication module, wherein the communication module includes one or more of a LoRa module, a 2.5G communication module, a 3G communication module, a 4G communication module and a 5G communication module.

4. A financial information processing system according to claim 1, characterized in that: The verification module (2) is connected to the main control module of the cloud management platform (7) via the verification management module (9).

5. A financial information processing system according to claim 4, characterized in that: The verification module (2) comprises a short message platform submodule, a CA center, and a biometric information verification submodule.

6. A financial information processing system according to claim 4, characterized in that: The verification management module (9) includes a security management module, a certificate management module and a short message gateway.

7. A financial information processing system according to claim 1, characterized in that: The encrypted transmission module (3) comprises an encryption layer, a transmission layer and a verification layer connected in sequence, the encryption layer is also connected to the verification module (2) and the cloud management platform (7), and the output end of the verification layer is connected to the cloud management platform (7) and the processing unit.

8. A financial information processing system according to claim 1, characterized in that: The intelligent customer service module (10) includes an information processing submodule and a personalized recommendation submodule.

9. A financial information processing method, applied to a financial information processing system according to any one of claims 1 to 8, characterized in that: include: The user logs in to the user terminal (1) and interacts with the intelligent customer service module (10), initiates a transaction request, and uses the verification module (2) to perform multi-factor authentication on the user. After the authentication is passed, the user information and the transaction request are transmitted to the processing module through the encryption transmission module (3); after being processed by the transaction processing module (4), the transaction is executed through the transaction unit (6); The data storage service module (801) is used to store real-time transaction data, verification data and user information during the transaction process, and the stored data is input into the abnormal transaction monitoring model in the abnormal transaction monitoring module (802) for classification prediction, and the abnormal detection result is input, and an early warning notification is sent according to the abnormal detection result; wherein, the abnormal transaction monitoring model is constructed based on a deep learning model.

10. A financial information processing method according to claim 9, characterized in that: The training process of the abnormal transaction monitoring model specifically includes: Acquire training data, wherein the training data includes transaction training data and corresponding anomaly detection labels; In the abnormal transaction monitoring model training module, an initial abnormal transaction monitoring model is constructed based on a long short-term memory network. The initial abnormal transaction monitoring model includes an input layer, an LSTM layer, and an output layer connected in sequence. The output layer is input into the initial abnormal transaction monitoring model, and the long-term dependency in the time series is captured by LSTM units (e.g., 50 units) in the LSTM layer. The probability that the current transaction is abnormal is output through the output layer to obtain an initial prediction result. The training is performed with the goal of minimizing the loss between the initial prediction result and the anomaly detection label corresponding to the transaction training data, thereby obtaining a trained abnormal transaction monitoring model. The trained abnormal transaction monitoring model is deployed in the abnormal transaction monitoring module (802) to process transaction data in real time.

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