A Comparative Study of Data Mining Techniques for Credit Scoring in Banking

• Main Authors: Shih-Chen Huang, 黃世禎
• Other Authors: Min-Yuh Day
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/15380705741747958620

碩士 === 淡江大學 === 資訊管理學系碩士班 === 101 === Credit is becoming one of the most important sources of income for the banking institutions. Prior studies indicated that logistic regression and neural network had been performed better on credit risk scoring. The major purpose of the present study is to propose appropriate credit risk scoring models to reduce credit risk and compare the accuracy of various classification models. The study proposed using enterprise data mining software to construct four classifications predictive models, such as decision tree, logistic regression, neural network and support vector machine, and further compared their accuracy of 17 classification models. The experimental results show that support vector machine classification models perform better in terms of high accuracy. The main contribution of this paper is that we use data mining techniques to construct various classification models for credit scoring in banking and compare their accuracy, and evidence shows that support vector machine outperforms traditional classification methods.