A personal credit default prediction method

Abstract

The invention discloses a personal credit default prediction method which specifically comprises the following steps: S1, collecting personal information data of a borrower and credit account activityinformation data to establish a database, removing missing values and abnormal values of the data, and preprocessing the data; S2, constructing a decision-making tree, forming a random forest by a plurality of combined classifiers of the decision-making tree, constructing a random forest model, and classifying credit data samples; S3, logical regression analysis and a random forest algorithm arecombined, the classification accuracy is improved, and personal credit default prediction is completed. According to the method, the diversity of personal data is more focused, information acquisitionand analysis are comprehensive, and the defect that the prior art depends on data fitting is overcome; The method has the advantages of wide application range, small individual influence and high prediction accuracy.

Description

technical field [0001] The invention belongs to the field of artificial intelligence, and in particular relates to a personal credit default prediction method. Background technique [0002] In personal credit lending behavior, the borrower has relatively comprehensive information, while the lender has limited knowledge of the borrower's information, and there is a serious problem of information asymmetry between the borrower and the lender. The existing credit default prediction methods are difficult to fit non-linear data, ignore the intersection and fusion effects of various value information, do not fully exploit the characteristics of complex information, and the prediction accuracy is low. Once a default occurs, the credit impact on the borrower Larger, but also affect the lender's economic operation. Contents of the invention [0003] In order to solve the above problems, the present invention proposes a method for predicting personal credit default behavior based o...

AI Technical Summary

Problems solved by technology

The existing credit default prediction methods are difficult to fit non-linear data, ignore the intersection and fusion effects of various value information, do not fully exploit the characteristics of complex information, and the prediction accuracy is low. Once a default occurs, the credit impact on the borrower Larger, but also affect the lender's economic operation

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