Small and medium-sized enterprise credit data mining method

A technology for small and medium-sized enterprises and data mining, applied in data processing applications, instruments, finance, etc., it can solve the problems of difficulty in credit feature selection and construction, heavy manual screening workload, and strict requirements for screening personnel experience, so as to assist business and credit The effect of decision-making, reducing default loss risk, and reducing operational risk

Pending Publication Date: 2020-12-15
天元大数据信用管理有限公司
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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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  • Small and medium-sized enterprise credit data mining method
  • Small and medium-sized enterprise credit data mining method
  • Small and medium-sized enterprise credit data mining method

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Embodiment Construction

[0057] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0058]SME credit evaluation is a quantitative process of data definition, collection, evaluation and analysis of SME credit risk, and credit characteristics are the quantitative performance results of SME credit characteristics. Feature combination and feature selection are the two main contents of feature engineering. Data and features are the key to machine learning, which determine the performance of machine learning models. Features play an important role in machine learning.

[0059] Generally speaking, the more the number of features, the more completely the attributes of the original data can be reflected, but the more the number of features, the better. Feature combination refers to the combination of some attributes of the original data through a series of calculation methods to generate some more expressive features. The method of fea...

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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...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q40/02G06K9/62
CPCG06Q40/03G06F18/22G06F18/24323G06F18/214
Inventor 崔光裕边松华崔乐乐
Owner 天元大数据信用管理有限公司
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