Customer rating method and device based on rejection inference and storage medium

Abstract

The invention relates to the technical field of information, and provides a customer rating method and device based on rejection inference and a storage medium. The objective of the invention is to speculate the specific performance of a rejected sample by using the overdue performance and rejected condition information of a customer so as to solve the problem of sample deviation. According to the main scheme, the method comprises the steps of obtaining a user passing sample and a rejected sample, and performing processing derivation of related characteristics on the samples; performing vintage analysis and rolling rate analysis on post-loan performance data of the application passing user, determining definition logic of a default target variable, and defining an application pass rejection target label according to a pass rejection user; performing initial modeling on the full sample and the application pass sample by using a rejection pass target label and a default target variable respectively, and calculating a deduced default label of a rejection user according to a KNN thought by using a user vector formed by dividing the full sample by using the two models; and fusing the rejected user deducing the default label and the passing user deducing the real default label to form a final training sample.

Description

technical field [0001] The invention relates to the field of information technology, and provides a customer rating method, device and storage medium based on rejection inference. Background technique [0002] When developing the default scoring model, most financial institutions can only obtain the data of users who have passed the application as training samples for modeling, while users whose applications are rejected are often excluded from the training due to lack of post-loan performance data and the inability to determine the default label. outside the sample. However, the application objects of the developed credit scoring model often lack all credit application users, including the rejected users and the approved users. In this way, the problem of sample bias arises, which eventually leads to biased model parameters when training the model. How to repair the post-loan performance data of rejected users is a problem of rejection inference that has plagued financial...

AI Technical Summary

Problems solved by technology

The inference method mainly uses the post-loan performance data of users who have applied for approval to infer the post-loan performance of rejected users at the technical level, and fails to make full use of the status of all users whose applications have been rejected.

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