A Decision Tree Classifier for Big Data Analytics on Credit Assessment Problem

Abstract

Credit assessment has been a large-scale problem among finance institutes. Their demand in reducing risk of customer debt can be achieved by applying data mining techniques to determine whether a new application should be approved or not. The problem, however, is actually under a Big Data environment. Complicated preprocessing steps are required because of the vast and messy data sources. The study proposes a Decision-Tree-Based Credit Assessment Approach (DTCAA) to solve the problem. Decision tree model is selected because of its interpretability and easily understanding rules, as well as its competitive performance. Additionally, the approach also includes various methods for data preprocessing. The consolidations can reduce messiness of the raw data, facilitating the implementation process. By acquiring the real data from one of the three biggest car collateral loan companies in Taiwan, the experiments indicate that decision Tree is competitive among various situations. Within multiple factors, the experiments suggest the usability of DTCAA in practice.