Small and medium-sized enterprise credit data mining method

Abstract

The invention discloses a small and medium-sized enterprise credit data mining method, relates to the technical field of big data and credit evaluation, and achieves small and medium-sized enterprisecredit data mining based on automatic feature engineering. Original credit feature data in a small and medium-sized enterprise training sample feature data set is preprocessed. Then, a feature subsetis formed through feature distance calculation, and feature linear combination and feature nonlinear combination are conducted on the feature subset; original credit features which are only subjectedto data preprocessing processing, linear combination features which are subjected to feature linear combination processing and nonlinear combination features which are subjected to feature nonlinear combination processing are taken as a training feature set, training is performed to form a baseline model, and feature importance sorting is performed according to a training result; features with predicted value are selected. The method can improve the credit feature mining efficiency of small and medium-sized enterprises, reduces the manual intervention, improves the effectiveness of credit feature mining results of small and medium-sized enterprises, and improves the credit evaluation accuracy of small and medium-sized enterprises.

Description

technical field [0001] The invention relates to the technical field of big data and credit evaluation, in particular to a credit data mining method for small and medium-sized enterprises. Background technique [0002] In the field of credit evaluation of small and medium-sized enterprises, credit characteristics are an important factor affecting the effect of credit evaluation of small and medium-sized enterprises. However, due to the complexity and diversity of SMEs’ ​​credit risks, the degree of correlation between different credit characteristics and different types of SMEs and SMEs’ ​​credit risks is very different. There are great difficulties in selection and construction, the workload of manual screening is too large, and the requirements for the experience of screening personnel are very strict. How to construct an automatic feature engineering credit data mining system for SMEs with good predictive effect is an urgent problem to be solved. Contents of the inventi...

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Problems solved by technology

However, due to the complexity and diversity of SMEs’ ​​credit risks, the degree of correlation between different credit characteristics and different types of SMEs and SMEs’ ​​credit risks is very different. There are great difficulties in selection and construction, the workload of manual screening is too large, and the requirements for the experience of screening personnel are very strict

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