Credit rating method based on classification rule classifier

Abstract

The invention discloses a credit rating method based on a classification rule classifier. The method comprises the following steps of S1, acquiring a user credit information set used for model training, respectively extracting characteristic attributes corresponding to all the information in the user credit information set to form a training set, and training by using a RIPPER classifier to obtaina RIPPER rating model; and S2, inputting credit information of a user to be evaluated and extracting the corresponding characteristic attributes, inputting the extracted characteristic attributes into the RIPPER rating model for classification, and obtaining a credit rating result output. The method has the advantages of being simple in implementation method, good in rating performance, high in rating efficiency, convenient to acquire a rating rule, easy to understand the rule and the like.

Description

technical field [0001] The invention relates to the technical field of credit evaluation, in particular to a credit evaluation method based on a classification rule classifier. Background technique [0002] Credit rating refers to the evaluation by an independent third-party credit rating intermediary agency on the debtor's ability and willingness to repay the debt principal and interest in full on schedule, and uses simple rating symbols to indicate the severity of its default risk and loss. The overall evaluation of the rating object's ability and willingness to fulfill relevant contracts and economic commitments. When the credit institution accepts the customer's credit application, it uses the characteristic variables in the application form submitted by the customer to establish a scoring model to obtain a credit value of the applicant, and compares this value with the standard value set in advance to judge the possibility of the borrower's overdue , so as to decide wh...

AI Technical Summary

Problems solved by technology

[0003] For users who newly submit applications, they need to perform credit ratings based on the relevant information provided. Currently, credit ratings for users mainly use credit scorecards or machine learning, but ratings based on scorecards are too general. However, the rating method based on machine learning is difficult to explain, and it is difficult for decision makers to intuitively understand the rules, resulting in difficult decision-making. Therefore, it is urgent to provide a credit rating method to Enables improved individual rating performance while obtaining rating rules that are easy for users to understand

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