Internet financial credit evaluation method based on PSO-BP neural network

Abstract

The invention discloses an Internet financial credit evaluation method based on a PSO-BP neural network, and the method comprises the steps: obtaining a result true value of information, carrying outthe normalization processing and principal component analysis dimensionality reduction of obtained data, dividing a test set and a training set, initializing the number of input nodes, the number of output nodes and the number of hidden layer nodes of the BP neural network; using a traditional gradient descent method and back propagation for continuously adjusting the weight and the threshold of anetwork to construct a BP neural network model, using a particle swarm algorithm for optimizing the connection weight and the threshold to obtain a PSOBP neural network model, and using a verification set for verification and optimization; and deploying the model to an application system to perform feature parameter extraction and prediction classification on data of a real-time application client. According to the invention, the convergence rate of the BP neural network is greatly improved, the obtained credit evaluation model of the PSO-BP neural network can accurately and quickly realize credit evaluation of an Internet financial applicant, the service timeliness of application approval is effectively improved, and the risk control cost and the application fraud risk are reduced.

Description

technical field [0001] The invention belongs to the technical field of risk control in the Internet financial industry, and specifically provides an Internet financial credit evaluation method using particle swarm optimization (Particle Swarm optimization, PSO) to optimize BP neural network. Background technique [0002] With the development of banks, third-party payment, P2P, and Internet lending platforms, Internet finance has an increasing demand for personal credit evaluation. In addition to using scorecard models based on various algorithms such as logistic regression, support vector machines, and random forests, The credit evaluation method based on neural network is a very large technical field. Among them, BP (Back Propagation) neural network is the most widely used because of its simple structure and strong applicability. [0003] BP neural network, also known as error backpropagation neural network, can use the error to reversely adjust the weight and threshold of...

AI Technical Summary

Problems solved by technology

Moreover, the number of individuals in the offspring population of the BP neural network optimized by the genetic algorithm is always the same as the number of individuals in the parent generation, resulting in a low probability of excellent individuals in the offspring, making the convergence speed slow and easy to fall into local optimum

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