Credit risk determination and data processing method, equipment, medium and program product

Abstract

Embodiments of the present application provide a credit risk determination and data processing method, device, medium, and program product. In the embodiment of the present application, feature extraction can be performed on the credit application data of the user to be tested and the credit application data of the sample user respectively to determine the credit feature of the user to be tested and the credit feature of the sample user; and according to the credit feature of the user to be tested and Calculate the similarity between the user to be tested and the sample user based on the credit characteristics of the sample user; determine the risk attribute of the user to be tested according to the similarity between the user to be tested and the sample user and the risk attribute of the sample user. Since the similarity between the user to be tested and the sample user is introduced when predicting the credit risk of the user to be tested, the reason why the user to be tested is determined as the current risk attribute can be explained according to the similarity between the user to be tested and the sample user. The reason makes the credit risk prediction results interpretable, which helps to meet the interpretability requirements of the credit risk control scenario.

Description

technical field [0001] The present application relates to the technical field of data processing, in particular to a credit risk determination and data processing method, device, medium and program product. Background technique [0002] The deep learning model can have stronger representation ability, and has achieved better model effects in many scenarios. However, on the one hand, the deep learning model has a relatively large demand for sample data, and the sample size in the credit risk control scenario is generally difficult to meet the data requirements of the deep network; on the other hand, the deep learning model is not interpretable enough to Meet the requirements of credit risk control scenarios for interpretability. Contents of the invention [0003] Various aspects of the present application provide a credit risk determination and data processing method, device, storage medium, and program product to meet the interpretability requirements of credit risk contr...

AI Technical Summary

Problems solved by technology

However, on the one hand, the deep learning model has a relatively large demand for sample data, and the sample size in the credit risk control scenario is generally difficult to meet the data requirements of the deep network; on the other hand, the deep learning model is not interpretable enough to Meet the interpretability requirements of the credit risk control scenario

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