Loaner credit rating method and system based on machine learning

Abstract

The invention discloses a loaner credit rating method and system based on machine learning. The method herein includes the following steps: acquiring original data for modeling, wherein the original data for modeling includes a credit report and an overdue shop list; extracting from the credit report and classifying indexes, obtaining a predication variable and the weight thereof; on the basis of the overdue shop list, the obtained prediction variable and the weight thereof, using the machine learning method to model, obtaining a prediction model of a response variable and the prediction variable; based on the obtained prediction model, predicting a new loaner, obtaining a default probability of the new loaner; based on the default probability of the new loaner, computing the credit rating of the new loaner. According to the invention, the method herein uses the machine learning method to model, is suitable for meeting fast changing requirements for loaner data under new circumstances; is provided with a step for extracting from the credit report and classifying indexes, therefore being more comprehensive and easier. According to the invention, the method can be widely applied to the field of computer applications.

Description

technical field [0001] The invention relates to the field of computer applications, in particular to a method and system for credit rating of loan users based on machine learning. Background technique [0002] Credit rating, also known as "credit rating" or "reputation evaluation", is an important content and basis for establishing a social credit system. According to the usual definition, credit rating is an objective and impartial position of a third party, based on a standardized evaluation index system, by a credit rating service agency, using scientific evaluation methods, and performing strict evaluation procedures. Social organizations and other market participants' credit records, corporate quality, management capabilities, operating levels, external environment, financial status, and development prospects, etc., are fully understood, investigated, and analyzed. It is an economic activity that makes a comprehensive evaluation of various possible risks, expresses its...

AI Technical Summary

Problems solved by technology

However, this credit rating method is based on historical experience, and its rating has a certain lag, which cannot reflect the situation of new users under the new situation, and the formulation and modification of its scoring rules need to go through strict expert demonstrations , the cycle of formulation and modification is generally longer, and the speed of data change is slow

In addition, the traditional credit rating method generally reads the credit report manually, or uses the credit reference interface for interface docking, which cannot directly analyze the credit report document, let alone subdivide the indicators in the credit report. Not comprehensive enough and convenient

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