Microexpression-based credit authorization method and device, terminal and readable storage medium

Abstract

The invention provides a microexpression-based credit authorization method. The microexpression-based credit authorization method includes the steps: acquiring a credit microexpression sample set, andconstructing a microexpression fraud identification model according to the credit microexpression sample set; when receiving a credit authorization instruction, acquiring an original video stream ofapplicant credit Q&A (Questions and Answers), wherein the original video stream includes microexpressions of the applicant credit Q&A process; inputting the original video stream into the microexpression fraud identification model to perform microexpression identification, and obtaining a microexpression identification result; and according to the microexpression identification result, generatingcorresponding credit decision-making suggestion information. The invention also provides a microexpression-based credit authorization device, equipment and a readable storage medium. The microexpression-based credit authorization method uses the microexpression fraud identification model to analyze the microexpressions of a credit applicant to determine the real feeling of the applicant to determine whether the applicant tell lies so as to detect fraud, thus reducing the workload of artificial authorization, and being conductive to improving efficiency and accuracy of credit authorization.

Description

Technical field [0001] The present invention relates to the field of credit, and in particular to a credit review method, device, terminal and readable storage medium based on micro-expressions. Background technique [0002] At present, when financial institutions on the market conduct loan review to loan applicants, experienced and capable reviewers conduct one-to-one interviews and signatures on the loan applicants based on the credit information they provide; in this process, approval The staff judges whether the applicant is lying or cheating based on the applicant's answers. [0003] However, in practice, the number of experienced and capable reviewers is small, and the efficiency of manual review is low, which makes it impossible to process a large number of credit applications in a timely manner; at the same time, manual face-to-face verification is often difficult to identify fraudulent applications. , The law of face-to-face audits is easily bypassed by black intermediari...

AI Technical Summary

Problems solved by technology

[0003] However, in reality, the number of experienced and capable reviewers is small, and the efficiency of manual review is low, so that it is impossible to deal with a large number of credit applications in a timely manner; at the same time, it is often difficult to identify the applicant's oral forgery. , the face-to-face review rules are easily bypassed by black intermediaries, resulting in failure to identify fraudulent behavior and increasing the bad debt rate

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