Loan risk early warning method and system based on multi-industry data

Abstract

The invention relates to the field of loan risk early warning and discloses a loan risk early warning method and system based on multi-industry data. The obtains user information related to a borrower from a plurality of angles by using multi-industry data; compares the user information with the historical information of the borrower to identify the abnormal behavior of the borrower; and classifies risk events according to abnormal information in an abnormity recognition result, and finally evaluates the loan risk of the borrower according to a preset risk index to determine the loan risk level of the borrower and output a warning report. The method and system can continuous monitor the borrower after the borrower loans by the multi-industry data, and can identify the loan with overdue tendency more accurately and effectively, make corresponding warning, raise the loan recovery rate and reduces the bad assets of credit and loan financial institutions such as the banks or the like.

Description

technical field [0001] The invention relates to the field of loan risk early warning, in particular to a loan risk early warning method based on cross-industry data. And a loan risk early warning system based on cross-industry data, the cross-industry data is relative to loan industry data, such as operator data, TravelSky data, UnionPay data and so on. Background technique [0002] In recent years, with the rapid development of the information industry and the popularization of the mobile Internet, more and more microfinance institutions, consumer finance companies, and P2P online lending financial institutions are trying their best to expand their businesses. However, due to the brutal expansion in the early stage, The risk control system is weak. While attracting customers with real needs, it has also brought many low-quality customers and fraudulent customers. Therefore, with the increase in business volume, the industry's non-performing rate has remained high. [0003]...

AI Technical Summary

Problems solved by technology

This kind of early warning report has strong limitations. The lender must have a loan on a multi-party platform, and the corresponding lending institutions are all in the warning data pool to have an early warning report, and the early warning time is relatively lagging. If the lender is in When there is bad information in other non-loan industries, the lender is very likely to have overdue behavior or potential overdue risk

[0004] In addition, due to the imperfect credit system of our country itself, and the data in the loan industry are rarely shared among institutions, it is difficult for many credit financial institutions to judge the quality of customers, and even a large number of small loan institutions completely It has become an isolated island of information, and there is a huge blind spot in the pre-loan review and post-loan management of customers, and the risk of credit overdue is completely uncontrollable

[0005] Therefore, risk warnings for lenders based solely on loan industry data cannot promptly and effectively identify whether lenders have overdue behavior or potential overdue risks

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