Distributed consumption credit risk control system based on AI large model and use method

Abstract

The invention relates to the field of financial science and technology, and discloses a distributed consumption credit risk control system based on an AI large model and a use method.The distributed consumption credit risk control system comprises edge computing nodes, a central server and a data communication mechanism, and the edge computing nodes are distributed on user terminal equipment or a local server of a bank; the central server is located in a central server of a cloud or a bank, and the data communication mechanism adopts an advanced encryption standard and other encryption technologies to ensure the data transmission security between the edge node and the central server. According to the distributed consumer credit risk control system based on the AI large model, through distributed calculation, real-time data processing and intelligent model optimization, the problems of data privacy, data islands, real-time performance, fraud detection, customer experience balance and other pain points in traditional financial risk control are effectively solved, the accuracy and real-time performance of risk control are improved, and the risk control efficiency is improved. And the operation cost is reduced, and the safety and compliance of the system are ensured.

Description

Technical Field

[0001] This invention relates to the field of financial technology, specifically to a distributed consumer credit risk control system based on an AI large-scale model and its usage method. Background Technology

[0002] Consumer credit is a product of financial innovation. It refers to loans launched by commercial banks for the personal consumption purposes (non-business purposes) of natural persons (legal persons or organizations). The introduction of personal consumer credit is one of the important measures in a series of comprehensive reforms of state-owned commercial banks to establish and improve themselves, adapt to financial system reforms, and adapt to the trend of international financial development. It breaks the limitations of traditional one-way financing between individuals and banks and creates a new creditor-debtor relationship of mutual financing between individuals and banks. With the rapid development of consumer credit business, financial institutions are facing increasingly complex risk management challenges.

[0003] Traditional centralized risk control systems rely primarily on centralized data processing and analysis. This approach not only faces challenges in data privacy and compliance but can also lead to delayed risk control decisions due to data transmission latency. Furthermore, traditional risk control systems struggle to adapt quickly to ever-changing risk environments and cannot update and optimize models in real time, resulting in limited risk control effectiveness. Modern risk control systems require greater real-time performance, flexibility, and protection of user privacy. Summary of the Invention

[0004] The purpose of this invention is to provide a distributed consumer credit risk control system based on an AI large-scale model. This system distributes the risk control process across edge computing nodes and optimizes strategies through an AI large-scale model, achieving isolation between the risk control model training and the actual usage environment. The system only needs to provide anonymized risk control statistical data to iterate the model and strategies, thereby improving risk control effectiveness while ensuring user privacy protection and system compliance.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a distributed consumer credit risk control system and method based on AI large model, including edge computing nodes, a central server and a data communication mechanism. The edge computing nodes are distributed on user terminal devices or local servers of banks, the central server is located in the cloud or in the central server of banks, and the data communication mechanism uses advanced encryption standards and other encryption technologies to ensure the security of data transmission between edge nodes and central server.

[0006] Preferably, the edge computing node is responsible for collecting, preprocessing, and performing preliminary risk control analysis of local data.

[0007] Preferably, the central server runs a large-scale trained AI model for deep analysis and strategy optimization.

[0008] Preferably, the data transmitted by the data communication mechanism is mainly anonymized risk control statistics to avoid leakage of sensitive user information.

[0009] Preferably, the central server uses historical data and de-identified data collected from edge nodes to train the AI ​​large model. The training process includes steps such as data cleaning, feature engineering, model building, verification, and optimization.

[0010] Preferably, when the edge computing node receives a consumer credit request, it uses a local model to conduct a preliminary risk assessment, and the preliminary analysis results can be directly used for credit approval or further review.

[0011] Preferably, the edge computing node feeds back the statistical data from the risk control decision to the central big data model server.

[0012] Preferably, the central server uses statistical data to optimize the model and adjust the strategy, continuously improving the accuracy and adaptability of the risk control model. The optimized model and strategy are then distributed to each edge node for updates.

[0013] A method for using a distributed consumer credit risk control system based on an AI large-scale model includes the following steps:

[0014] S1. Data Collection and Preprocessing: Edge nodes collect multi-dimensional data such as user transaction data, device information, and geographical location, and preprocess the data to generate input features for the risk control model. Preprocessing includes steps such as data cleaning, noise reduction, and standardization.

[0015] S2, Local Risk Control Decision: Edge nodes use local risk control models to assess the risk of consumer credit requests. The model generates risk scores and decision suggestions based on input features and sends the results to the bank's credit approval system. This process is completed in milliseconds to ensure real-time performance.

[0016] S3. Model Iteration and Optimization: Regularly collect statistical data (such as risk score distribution, decision accuracy, etc.) from each edge node and summarize them to the central server. The central server uses this data to optimize the model and adjust the strategy. The optimized model parameters are distributed to each edge node through an encrypted channel to update the local model.

[0017] Compared with the prior art, the beneficial effects of the present invention are:

[0018] 1. This AI-based distributed consumer credit risk control system executes local risk control tasks at edge nodes and utilizes a large model on a central server for strategy optimization, achieving real-time performance and high efficiency while protecting user privacy. Through the localization of risk control tasks and the centralization of model training, it minimizes data transmission and enables efficient model updates. The system architecture features a modular design, supporting flexible expansion and dynamic adjustment to adapt to consumer credit risk control applications of different scales and needs.

[0019] 2. This AI-based distributed consumer credit risk control system effectively addresses pain points in traditional financial risk control, such as data privacy, data silos, real-time performance, fraud detection, and balancing customer experience, through distributed computing, real-time data processing, and intelligent model optimization. The system not only improves the accuracy and real-time performance of risk control but also reduces operating costs and ensures system security and compliance. In the future, with the continuous development of edge computing and AI technologies, this system is expected to play an even more important role in the field of financial risk control, providing financial institutions with more powerful risk management tools. Detailed Implementation

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] This invention provides a technical solution: a distributed consumer credit risk control system based on an AI large model, comprising edge computing nodes, a central server, and a data communication mechanism. The edge computing nodes are distributed on user terminal devices or local servers of banks, the central server is located in the cloud or in the bank's central server, and the data communication mechanism uses advanced encryption standards and other encryption technologies to ensure the security of data transmission between edge nodes and the central server.

[0022] In this embodiment, the edge computing nodes are responsible for collecting, preprocessing, and performing preliminary risk control analysis of local data. These nodes run lightweight risk control models and can make preliminary risk assessments and decisions locally in real time, thereby reducing data transmission volume and response time. Edge computing nodes make full use of local computing resources, reduce dependence on central servers, and improve the overall response speed and processing capacity of the system.

[0023] In this embodiment, the central server runs a large-scale trained AI model for deep analysis and strategy optimization. It is responsible for training and optimizing the risk control model and formulating global strategies, and distributing them to edge computing nodes.

[0024] In this embodiment, the data transmitted by the data communication mechanism is mainly anonymized risk control statistics to avoid leakage of sensitive user information and ensure the confidentiality and integrity of the data during transmission.

[0025] In this embodiment, the central server uses historical data and de-identified data collected from edge nodes to train a large AI model. The training process includes steps such as data cleaning, feature engineering, model building, verification, and optimization.

[0026] In this embodiment, when the edge computing node receives a consumer credit request, it uses a local model to conduct a preliminary risk assessment. The preliminary analysis results can be directly used for credit approval or further review. The model generates a risk score and decision suggestions based on the input features and sends the results to the bank's credit approval system. This process is completed within milliseconds to ensure real-time performance.

[0027] In this embodiment, the edge computing node feeds back statistical data (such as risk score distribution, decision accuracy, etc.) from risk control decisions to the central large model server. This data is used to optimize and iterate the AI ​​large model.

[0028] In this embodiment, the central server uses statistical data to optimize the model and adjust the strategy, continuously improving the accuracy and adaptability of the risk control model. The optimized model and strategy are then distributed to each edge node for updates.

[0029] According to another aspect of the present invention, a method for using a distributed consumer credit risk control system based on an AI large model is provided, comprising the following steps:

[0030] S1. Data Collection and Preprocessing: Edge nodes collect multi-dimensional data such as user transaction data, device information, and geographical location, and preprocess the data to generate input features for the risk control model. Preprocessing includes steps such as data cleaning, noise reduction, and standardization.

[0031] S2, Local Risk Control Decision: Edge nodes use local risk control models to assess the risk of consumer credit requests. The model generates risk scores and decision suggestions based on input features and sends the results to the bank's credit approval system. This process is completed in milliseconds to ensure real-time performance.

[0032] S3. Model Iteration and Optimization: Regularly collect statistical data (such as risk score distribution, decision accuracy, etc.) from each edge node and summarize them to the central server. The central server uses this data to optimize the model and adjust the strategy. The optimized model parameters are distributed to each edge node through an encrypted channel to update the local model.

[0033] Working principle:

[0034] 1. Model training and updates:

[0035] AI large-scale model training: The central server uses historical data and de-identified data collected from edge nodes to train the AI ​​large-scale model. The training process includes steps such as data cleaning, feature engineering, model building, verification and optimization.

[0036] Model distribution and updates: The optimized risk control model is distributed to various edge computing nodes through an encrypted channel. The model is updated incrementally to reduce network bandwidth requirements and update latency.

[0037] 2. Breakdown and isolation of the risk control process:

[0038] Localized risk control: When an edge node receives a consumer credit request, it uses a local model to conduct a preliminary risk assessment. The preliminary analysis results (such as risk scores) can be directly used for credit approval or further review.

[0039] Statistical data feedback: Edge nodes feed back statistical data (such as risk score distribution, decision accuracy, etc.) from risk control decisions to the central big model server. This data is used to optimize and iterate the AI ​​big model.

[0040] Strategy optimization and iteration: The central server uses statistical data to optimize the model and adjust the strategy, continuously improving the accuracy and adaptability of the risk control model. The optimized model and strategy are then distributed to each edge node for updates.

[0041] 3. Compliance and Privacy Protection:

[0042] Data minimization principle: The system design follows the data minimization principle. Sensitive user information is processed only at local edge nodes, and the central server only receives necessary statistical data to protect user privacy.

[0043] Anonymization and desensitization techniques: Before data transmission, data is anonymized and desensitized to ensure that personal identity information cannot be derived during transmission and storage.

[0044] Encrypted transmission: All data transmission uses advanced encryption technologies such as AES to ensure the confidentiality and integrity of data during transmission.

[0045] 4. System performance and reliability:

[0046] High-performance computing: Edge computing nodes make full use of local computing resources, reduce dependence on central servers, and improve the overall response speed and processing power of the system;

[0047] High stability: The system adopts a redundant design to ensure that the overall risk control function can still operate normally even if some edge nodes fail. The central server can dynamically adjust the model distribution and update frequency according to network conditions to ensure system stability.

[0048] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A distributed consumer credit risk control system based on an AI large-scale model, comprising edge computing nodes, a central server, and a data communication mechanism, characterized in that: The edge computing nodes are distributed on user terminal devices or local servers of the bank, while the central server is located in the cloud or in the bank's central server. The data communication mechanism uses advanced encryption standards and other encryption technologies to ensure the security of data transmission between the edge nodes and the central server.

2. The distributed consumer credit risk control system based on AI large model according to claim 1, characterized in that: The edge computing node is responsible for collecting, preprocessing, and performing preliminary risk control analysis of local data.

3. The distributed consumer credit risk control system based on AI large model according to claim 1, characterized in that: The central server runs large-scale trained AI models for deep analysis and strategy optimization.

4. The distributed consumer credit risk control system based on AI large model according to claim 1, characterized in that: The data communication mechanism mainly transmits anonymized risk control statistics to prevent the leakage of sensitive user information.

5. The distributed consumer credit risk control system based on AI large model according to claim 1, characterized in that: The central server uses historical data and de-identified data collected from edge nodes to train a large AI model. The training process includes steps such as data cleaning, feature engineering, model building, verification, and optimization.

6. The distributed consumer credit risk control system based on AI large model according to claim 1, characterized in that: When the edge computing node receives a consumer credit request, it uses a local model to conduct a preliminary risk assessment. The preliminary analysis results can be directly used for credit approval or further review.

7. The distributed consumer credit risk control system based on AI large model according to claim 1, characterized in that: The edge computing nodes feed back statistical data from risk control decisions to the central large model server.

8. The distributed consumer credit risk control system based on AI large model according to claim 1, characterized in that: The central server uses statistical data to optimize models and adjust strategies, continuously improving the accuracy and adaptability of the risk control model. The optimized models and strategies are then distributed to each edge node for updates.

9. A method of using an AI-based large-scale model distributed consumer credit risk control system, applied to the AI-based large-scale model distributed consumer credit risk control system described in any one of claims 1-8, characterized in that, Includes the following steps: S1. Data Collection and Preprocessing: Edge nodes collect multi-dimensional data such as user transaction data, device information, and geographical location, and preprocess the data to generate input features for the risk control model. Preprocessing includes steps such as data cleaning, noise reduction, and standardization. S2, Local Risk Control Decision: Edge nodes use local risk control models to assess the risk of consumer credit requests. The model generates risk scores and decision suggestions based on input features and sends the results to the bank's credit approval system. This process is completed within milliseconds to ensure real-time performance. S3. Model Iteration and Optimization: Regularly collect statistical data (such as risk score distribution, decision accuracy, etc.) from each edge node and summarize them to the central server. The central server uses this data to optimize the model and adjust the strategy. The optimized model parameters are distributed to each edge node through an encrypted channel to update the local model.