MCDM approach to evaluating bank loan default models

Abstract

Banks and financial institutions rely on loan default prediction models in credit risk management. An important yet challenging task in developing and applying default classification models is model evaluation and selection. This study proposes an evaluation approach for bank loan default classification models based on multiple criteria decision making (MCDM) methods. A large real-life Chinese bank loan dataset is used to validate the proposed approach. Specifically, a set of performance metrics is utilized to measure a selection of statistical and machine-learning default models. The technique for order preference by similarity to ideal solution (TOPSIS), a MCDM method, takes the performances of default classification models on multiple performance metrics as inputs to generate a ranking of default risk models. In addition, feature selection and sampling techniques are applied to the data pre-processing step to handle high dimensionality and class unbalancedness of bank loan default data. The results show that K-Nearest Neighbor algorithm has a good potential in bank loan default prediction.

Keywords:

• default risk model
• bank loan
• machine learning
• multiple criteria decision making (MCDM)
• model selection

JEL Classification:

• D81
• H81
• G32

Additional information

Notes on contributors

Gang Kou

Gang KOU is a Professor and Executive Dean of School of Business Administration, Southwestern University of Finance and Economics. He is the Managing Editor of International Journal of Information Technology & Decision Making and series Editor of Quantitative Management (Springer). Previously, he was a Professor of School of Management and Economics, University of Electronic Science and Technology of China, and a Research Scientist in Thomson Co., R&D. He received his PhD in Information Technology from the College of Information Science & Technology, University of Nebraska at Omaha; got his Master’s degree in Department of Computer Science, University of Nebraska at Omaha; and BS degree in Department of Physics, Tsinghua University, Beijing, China. He has participated in various data mining projects, including data mining for software engineering, network intrusion detection, health insurance fraud detection and credit card portfolio analysis. He has published more than eighty papers in various peer-reviewed journals and conferences. He has been Keynote speaker/workshop chair in several international conferences. He co-chaired Data Mining contest on The Seventh IEEE International Conference on Data Mining 2007 and he is the Program Committee Co-Chair of the 20th International Conference on Multiple Criteria Decision Making (2009) and NCM 2009: 5th International Joint Conference on INC, ICM and IDC. He is also co-editor of special issues of several journals, such as Journal of Multi Criteria Decision Analysis, Decision Support Systems, Journal of Supercomputing and Information Sciences.

Yi Peng

Yi PENG is a Professor of School of Management and Economics, University of Electronic Science and Technology of China. She received her PhD in Information Technology from the College of Information Science & Technology, University of Nebraska at Omaha and got her Master’s degree in Department of Info. Science & Quality Assurance, University of Nebraska at Omaha and BS degree in Department of Management Information Systems, Sichuan University, China. Her research interests cover knowledge discover in database and data mining, multi-criteria decision making, data mining methods and modelling, knowledge discovery in real-life applications.

Chen Lu

Chen LU got his Master’s degree in Management Science from School of Management and Economics, University of Electronic Science and Technology of China.