Risk prediction system and method for optimizing NARX neural network through ant lion algorithm

Abstract

The invention discloses a risk prediction system and method for optimizing NARX neural network through an ant lion algorithm, wherein the system comprises a user side and a server side, the user side comprises an information acquisition module and a risk prediction initiation module, and the server side comprises an information processing module, a database and a risk prediction module; the information acquisition module is used for a user to acquire customer data and integrate the customer data into customer data; the risk prediction initiating module is used for a user to initiate a risk prediction application request; the information processing module is used for acquiring customer data and storing the customer data in a database, and is also used for acquiring and auditing the risk prediction application request and generating auditing information transmitted to the user side and the risk prediction module; the database is used for storing customer data; and the risk prediction module is used for acquiring audit information and acquiring customer data in the database according to the audit information, and is also used for performing risk prediction on the customer data to obtain customer overdue risk prediction data. The method comprises steps A1-A6.

Description

technical field [0001] The invention belongs to the technical field of Internet finance, and in particular relates to an antlion algorithm optimized NARX neural network risk prediction system and method. Background technique [0002] In order to meet the credit risk control needs at different stages, financial institutions usually need to use the application score card before the loan, the behavior score card during the loan, and the collection score card after the loan to score the risk of financial users. Among them, the loan behavior score card model is based on various behaviors of financial users during the use of the account, and evaluates the customer's repayment ability by predicting the customer's default risk based on the customer's historical behavior characteristic data in the loan and repayment willingness, etc., according to the probability of default to monitor loan behavior, and dynamically predict the scoring model of financial user loan risk. [0003] The ...

AI Technical Summary

Problems solved by technology

[0005] However, the NARX neural network is the same as other neural networks, and the network performance is easily affected by the initial value. The existing technology mainly uses genetic, particle swarm, ant colony and other algorithms to optimize the initial value of the network, but it is easy to fall into local optimum and slow convergence speed. Problems, how to make the NARX neural network jump out of the local optimum, improve the convergence speed, and how to achieve a balance between global exploration and local development capabilities are still difficult

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