CS-PNN-based customer credit risk assessment method and system

Abstract

The invention relates to the technical field of risk control of the Internet financial industry, in particular to a CS-PNN-based customer credit risk assessment method and system. Compared with BP andRBF neural networks, the PNN fuses a Bayesian decision theory and density function estimation on the basis of a radial basis function, the method has the advantages of simple network structure, few adjustment parameters, short operation time, no local minimum point and the like; compared with GA, PSO, ACO and other optimization algorithms, the CS algorithm searches for a global optimal solution by simulating the combination of the parasitic propagation behavior of the valley bird nest and the Levy flight search principle, has the advantages of being few in parameter setting, high in convergence speed, high in universality and robustness, easy to implement and the like, and can efficiently balance local search and global search of the algorithm; the CSPNN model obtained by optimizing the smoothing factor of the PNN by the CS has the advantages of simple network structure, high convergence rate, good fault tolerance, high robustness, high classification accuracy, strong sample appendingcapability and the like, and can meet the requirement of real-time credit risk assessment of a loan system.

Description

technical field [0001] The invention belongs to the technical field of risk control in the Internet financial industry, and specifically provides a CS-PNN-based customer credit risk assessment method and system. Background technique [0002] With the rapid development of Internet finance, the assessment of customer credit risk has become an important research field. Credit risk assessment uses information submitted by loan or credit card applicants and information provided by third parties to calculate the applicant's credit risk, classify them into different risk levels, and use this as the basis for loan or credit card approval. [0003] Credit evaluation is essentially a classification problem in pattern recognition. According to the characteristics of applicants, such as age, gender, marital status and income, etc., machine learning methods are used to classify applicants into customers with different credit levels. When training the model, discover the rules based on t...

AI Technical Summary

Problems solved by technology

The existing technology uses swarm intelligent optimization algorithms, such as genetic algorithm, particle swarm optimization algorithm, and ant colony algorithm to automatically optimize the smoothing factor, but they still have some limitations in the nature of the target problem, parameter adjustment, calculation time, etc., and most of them are biased towards the global The search ability may be partial to the local search ability, and few can strike a balance between the two, and there are problems in the learning and training such as long learning process, easy to mature, easy to fall into local optimum, poor robustness, etc., learning speed, Learning accuracy and optimization efficiency are not high

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