Hybrid machine learning credit scoring model building method

Abstract

The invention discloses a hybrid machine learning credit scoring model building method. The method comprises the steps of 1, determining client risk classification standards based on a loan client historical data set; 2, based on the loan client historical data set, obtaining a loan client data feature set through feature extraction; 3, selecting at least two model algorithms from an alternative model library, building corresponding models based on the selected algorithms, performing model performance check on the built models by adopting a K-fold cross check method, performing standard check on the models about to pass the model performance check based on model check standards, obtaining evaluation index statistical quantity values, and selecting a model type used by final modeling according to the evaluation index statistical quantity values returned by the standard check of the models; and 4, based on the algorithm corresponding to the selected model type, building a credit scoring model. The technical effect of efficiently and accurately finishing user credit evaluation through the built hybrid machine learning credit scoring model is achieved.

Description

Technical field [0001] The present invention relates to the field of credit intelligence evaluation, in particular to a method for constructing a hybrid machine learning credit scoring model. Background technique [0002] my country's personal retail credit industry is booming, and the scale of loans continues to expand in the areas of credit cards, housing loans, auto loans, personal student loans, and consumer durables loans. Opportunities come with risks. Behind the rapid development of the small and micro-credit industry, there are also huge risks, especially credit risks. Risks cannot be eliminated. We can only use more scientific means to accurately assess risks, effectively control risks with correct strategies, and manage risks comprehensively with best operations, so as to maintain the stability and safety of the financial system. [0003] Credit scoring technology was born for this purpose. It uses modern mathematical statistical model technology to discover, analyze and...

AI Technical Summary

Problems solved by technology

[0008] The invention provides a method for constructing a hybrid machine learning credit scoring model, which solves the technical problems of poor accuracy and efficiency in traditional credit scoring technology, and realizes the construction of a hybrid machine learning credit scoring model that can efficiently and accurately The technical effect of completing user credit evaluation

[0009] Hybrid machine learning credit scoring can effectively solve the problems of traditional credit scorecard technology. It uses computational thinking as the core, adopts machine learning algorithms, and spans four major fields: unsupervised clustering, supervised classification, semi-supervised learning and reinforcement learning. New data technology application method

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