The Application of Tree-based model to Unbalanced German Credit Data Analysis

Abstract

With the development of financial consumption, demand for credit has soared. Since the bank has detailed client data, it is important to build effective models to distinguish between high-risk groups and low-risk groups. However, traditional credit evaluation methods including expert opinion, credit rating and credit scoring are very subjective and inaccurate. Moreover, the data are highly unbalanced since the number of high-risk groups is significantly less than that of low-risk groups. Progress in machine learning makes it possible to conduct accurate credit analysis. The tree-based machine learning models are particularly suitable for the unbalanced credit data by weighting the credit individuals. We apply a series of tree-based machine learning models to analyze the German Credit Data from the UCI Repository of Machine Learning Databases.