Early warning information push method, device, computer device and medium

Abstract

The present application relates to a big data analysis-based early warning information push method, an early warning information push device, a computer device and a storage medium. The method includes the following steps that: the risk data of a target customer are acquired; the risk data are quantified, so that a plurality of risk indicators are obtained; the risk indicators are inputted into apreset risk prediction model, so that a first risk score is obtained; a plurality of rule expressions are obtained, the rule expressions are adopted to perform risk prediction on the risk data, so that a second risk score can be obtained; and the first risk score and the second risk score are compared with each other, so that a high score value is obtained; and an early warning prompt is generatedaccording to the high score value, and the early warning prompt is pushed to a monitoring terminal. With the method adopted, the reliability of early warning information can be improved, and the false negative rate of risks can be reduced.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to a method, device, computer equipment and medium for pushing early warning information. Background technique [0002] In order to avoid risks, financial institutions involved in the loan business need to provide customer risk warnings to customers after lending. Customer risk early warning refers to the early detection of loan risks through on-site inspection or off-site inspection and other post-loan inspections using a combination of quantitative and qualitative analysis methods, and pushes early warning information to business personnel based on the discovered loan risks. In the traditional way, the early warning information is mainly obtained by the risk control personnel to track the customer's risk, and judge the customer's credit risk status based on the customer-related information learned during the tracking process and the established judgment rules. However...

AI Technical Summary

Problems solved by technology

However, different customers have different attributes and characteristics. For all customers, the established rules are uniformly used for risk judgment, which makes the reliability of early warning information poor, and the phenomenon of risk underreporting often occurs.

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