Post-loan early warning and monitoring system based on knowledge map technology

Abstract

The invention relates to the technical field of loan risk prevention and control system, in particular to a post-loan early warning monitoring system based on knowledge map technology. Includes a borrower information collection module, wherein the borrower information collection module is used for collecting the related information of the borrower; An associated person analysis module, wherein theassociated person analysis module is used for analyzing and screening the personnel associated with the borrower according to the associated information of the borrower, and forming a knowledge map of the associated personnel with the borrower as the core; An association degree calculation module, wherein the association degree calculation module is used for calculating an association degree score between the borrower and the associated person according to the knowledge map of the associated person; The associated person risk monitoring module is used for monitoring the risk event of the associated person. The after-loan early warning monitoring system based on the knowledge map technology provided by the invention can acquire the risk tendency of the borrower in advance, so as to take measures for the borrower as early as possible and reduce unnecessary losses of the bank.

Description

technical field [0001] The invention relates to the technical field of loan risk prevention and control systems, in particular to a post-loan early warning and monitoring system based on knowledge graph technology. Background technique [0002] At present, the post-loan early warning and monitoring work for small (50,000 yuan and below) personal operating loan revolving credit products (baitiao) on e-commerce platforms mainly uses methods such as regular or irregular scanning of specific indicators, making behavior score cards, etc. people to manage. This method can effectively predict and find some problematic borrowers, but this method is only for the detection of the borrowers themselves. Because the borrowers have problems such as small business scale, incomplete information, and some no fixed business premises, through this method When predicting risks, the timeliness and forward-lookingness of the predictions are slightly insufficient. When a certain borrower is hit b...

AI Technical Summary

Problems solved by technology

This method can effectively predict and find some problematic borrowers, but this method is only for the detection of the borrowers themselves. Because the borrowers have problems such as small business scale, incomplete information, and some no fixed business premises, through this method When predicting risks, the timeliness and forward-lookingness of the predictions are slightly insufficient. When a certain borrower is hit by the analysis of this method, the risk of the borrower is often already high, and the occurrence of risk events cannot be avoided, and the probability of loan overdue is high. Therefore, its forward-looking is poor, and it is impossible to detect the risk trend of the borrower in time in the early stage, which will cause losses to the bank.

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