Credit evaluation method for optimizing generalized regression neural network based on grey wolf algorithm

A neural network and generalized regression technology, applied in the risk control field of the Internet finance industry, can solve problems such as mutation, long training time, and falling into local extreme areas.

CN112581263AInactive Publication Date: 2021-03-30百维金科(上海)信息科技有限公司
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2021-03-30
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention relates to the technical field of risk control of the Internet financial industry, in particular to a credit evaluation method for optimizing a generalized regression neural network based on a grey wolf algorithm. The method comprises six steps, and compared with common BP and RBF neural networks, the method has the advantages that GRNN selected by the method is strong in nonlinear mapping capability, good in approximation performance and suitable for processing unstable data. The method has the advantages of being good in generalization ability, high in fitting ability, high intraining speed, convenient in parameter adjustment and the like, and compared with common optimization algorithms such as genetic algorithms and particle swarms, the grey wolf algorithm is few in parameter and simple in programming, and has the advantages of being high in convergence speed, high in global optimization ability, potential in parallelism, easy to implement and the like. The grey wolfalgorithm is adopted to optimize the GRNN network model, the prediction precision and stability are high, the defects that the GRNN prediction result is unstable and is very likely to fall into the local minimum value are effectively avoided, and rapid and accurate online real-time prediction of the credit score of the application user is achieved.
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Description

technical field

[0001] The invention relates to the technical field of risk control in the Internet financial industry, and specifically relates to a credit evaluation method based on a gray wolf algorithm to optimize a generalized regression neural network. Background technique

[0002] In terms of evaluation methods, the massive data and fast response requirements of Internet finance make artificial intelligence methods the best choice, and machine learning algorithms such as logistic regression, decision trees, support vector machines, and Bayesian networks have been successfully applied in various empirical studies . With the rapid development of artificial intelligence technology, neural networks such as error backpropagation (BP), radial basis function (RBF), and self-organizing map (SOM) have become important research fields for Internet financial credit evaluation. However, BP and RBF neural networks have the disadvantages of slow learning speed, easy to fall into l...

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Embodiment Construction

[0082] see figure 1 , the present invention provides a technical solution:

[0083] A credit evaluation method based on the gray wolf algorithm to optimize the generalized regression neural network, including the following six steps:

[0084]S1. Collect data, select a certain proportion and quantity of normal repayment and overdue customers from the back end of the Internet financial platform according to the post-loan performance as modeling samples, collect the basic personal information of the sample customers when they apply for account registration, and obtain operational behavior embedded in the monitoring software The point data is used as credit data, and the normal repayment or overdue performance corresponding to the sample is used as label data;

[0085] S2. Data preprocessing, after performing missing completion, outlier processing and normalization processing on the collected credit data, feature selection is performed on the credit data through the random forest...