Credit risk early warning method and device

Abstract

The invention discloses a credit risk early warning method and device, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring basic index data of a user, wherein the basic index data of the user comprises a plurality of first-level indexes for credit risk assessment; determining a credit risk influence value of each first-level index of the user based on a Bayesian classifier principle; aggregating the credit risk influence values of the first-level indexes belonging to the same second-level index to obtain the credit risk influence value of each second-level index of the user; and determining a credit risk assessment value of the user according to the credit risk influence value of each secondary index of the user. Through the above steps, the efficiency of credit risk assessment work and the accuracy of a credit risk assessment result can be improved, so that related institutions can find credit risk customers timely and effectively, and early warning is performed in advance to solve the credit risk.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to a credit risk early warning method and device. Background technique [0002] Credit risk has always been the focus of commercial banks to guard against in their business activities, and credit business is also the focus of analysis and monitoring by the internal audit department of commercial banks. In the era of informationization, data has become an important factor in the business activities of commercial banks. To discover and predict the risk status of credit customers in advance, it is necessary to collect credit customer transaction data stored in various business systems, and then use some statistical tools to carry out risk analysis on these business data, so as to obtain corresponding quantitative risk analysis results. Prompt the audited institution to take corresponding measures to prevent and resolve the risk. [0003] In the prior art, credit risk ...

AI Technical Summary

Problems solved by technology

Alternatively, audit professionals manually import the credit data to be analyzed, and then use tools such as excel to select data methods such as reliability analysis method and entropy value method to analyze the data. There is currently no dedicated automatic credit risk analysis system

[0004] In the process of realizing the present invention, the inventors of the present invention found that the existing credit risk assessment method has at least the following disadvantages: In the existing credit risk assessment method, the cost of manual participation is extremely high, and the threshold requirements for analysts are relatively high , the quality of the evaluation results often depends on the technical level of the analysts, and because the manual credit data analysis process has a high probability of error, which reduces the accuracy of the credit risk evaluation results. At the same time, manual credit data analysis and The process of processing is time-consuming, which reduces the efficiency of credit risk assessment

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