Credit risk monitoring method, device and equipment and storage medium

Abstract

The invention discloses credit risk monitoring method, device and equipment, and a storage medium. The credit risk monitoring method comprises the steps: collecting original data from a data source, cleaning the original data according to a preset cleaning strategy, and obtaining cleaning data; performing identity normalization processing on the cleaning data according to user identifiers in the cleaning data to obtain a first user data set with the same user identifier; according to a preset mapping rule, replacing the risk event in the first user data set with a reason code corresponding to the risk event to obtain a second user data set; and generating a credit risk form according to the second user data set. According to the invention, processing such as data cleaning, identity normalization, event cause coding and the like can be carried out on the original data, so that an accurate, ordered and integrated data set is provided for credit risk monitoring, and the accuracy and comprehensiveness of business processing are improved.

Description

technical field [0001] The present invention relates to the technical field of financial technology (Fintech), in particular to a credit risk monitoring method, device, equipment, system and storage medium. Background technique [0002] In recent years, with the rapid development of the financial technology (Fintech) industry, more and more microfinance institutions, consumer finance companies, and online lending financial institutions are trying their best to expand their business, but with the rapid increase of real demand customers At the same time, it is also accompanied by a large number of low-quality high-risk customers. [0003] At present, most lending platforms such as banks or credit institutions rely on the historical credit data of the lending platform for the credit evaluation of borrowers. Generally, only when the borrower has overdue data in the historical credit data of the lending platform, the Relevant early warnings will be issued. Such early warnings ar...

AI Technical Summary

Problems solved by technology

[0003] At present, most lending platforms such as banks or credit institutions rely on the historical credit data of the lending platform for the credit evaluation of borrowers. Generally, only when the borrower has overdue data in the historical credit data of the lending platform, the Relevant early warnings will be issued. Such early warnings are limited to the historical data of the loan industry, and cannot comprehensively judge the credit risk of the borrower, and the accuracy is not high enough

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