A method and a device for risk rating of applicants based on artificial intelligence

Abstract

The invention relates to a method and a device for risk rating of an applicant based on artificial intelligence. The method comprises the following steps: receiving a loan request of an applicant; Collecting data associated with the applicant; Determining, based in part on the data, whether to accept the applicant's loan request; Under the condition that the loan request of the applicant is received, classifying the applicant into one client group in one or more client groups based on the data; Matching loans of corresponding loan types and corresponding risk scoring models for the applicantson the basis of the client group; And calculating a risk score for issuing the loan to the applicant based on the risk score model, wherein the risk score is used for indicating the risk rating of therisk generated when the loan of the loan type is issued to the applicant.

Description

technical field [0001] The present invention relates to artificial intelligence analysis technology based on big data, and more particularly to a method and device for risk rating of applicants based on artificial intelligence. Background technique [0002] Loan applicants can be collectively referred to as small enterprises, micro enterprises, family workshop enterprises, and individual industrial and commercial households. For a long time, applicants have not been welcomed by the traditional financial industry due to reasons such as difficult income determination, light assets without collateral, and few credit records. Therefore, they often face the problem of "difficult and expensive financing" during the development process, thus Greatly restrict the growth of applicants. However, applicants are the new force of the national economy and play an important role in stabilizing growth, expanding employment, promoting innovation, prospering the market and meeting the variou...

AI Technical Summary

Problems solved by technology

The former credit granting model mainly takes the personal qualifications of the business owner as the assessment object, and the credit amount is not high enough to meet the needs of business operations, so it has no obvious advantage over personal loans

The process of the latter credit granting model is lengthy and complicated, requiring a large amount of offline manpower to participate, and the operation mode is close to the traditional financial industry, which cannot reflect the advantages of mutual funds

Therefore, so far, consumer finance products that are generally popular in the mutual fund industry are still targeted at individual customers, and there is no particularly efficient and fast solution for applicants

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