Title: Application and comparison of neural network, C5.0, and classification and regression trees algorithms in the credit risk evaluation problem (case study: a standard German credit dataset)

Authors: Mahdi Massahi Khoraskani; Fahimeh Kheradmand; Alireza Arshadi Khamseh

Addresses: Department of Industrial Engineering and Management Sciences, Amirkabir University of Technology, Tehran, Iran ' Department of Industrial Engineering and Management Sciences, Amirkabir University of Technology, Tehran, Iran ' Department of Industrial Engineering, College of Engineering, University of Kharazmi, Tehran, Iran

Abstract: Due to the reducing global economic stability, the demand of banks for predicting their customer's credit risk has significantly increased and has become more critical, still challenging than ever. This paper addresses the problem of credit risk evaluation of bank's customers utilising data mining tools. Three classification techniques include: neural network, C5.0, and classification and regression trees (CART) algorithms. In order to evaluate the performance of the classification techniques, an innovative two-stage evaluation process is proposed. Firstly, the optimal status of algorithms is found by tuning its parameters. Secondly, these tuned algorithms are ranked by the analytical hierarchy process (AHP) method while four criteria of overall accuracy, precision, sensitivity, and specificity are considered. As a case study, a standard German credit dataset are used to validate the performance of the proposed algorithms. It is illustrated that the neural network algorithm is the superior algorithm to evaluate bank customers' credit risk.

Keywords: credit risk evaluation; data mining; classification; neural networks; C5.0; classification and regression trees; CARTs; analytical hierarchy process; AHP.

DOI: 10.1504/IJKEDM.2017.091013

International Journal of Knowledge Engineering and Data Mining, 2017 Vol.4 No.3/4, pp.259 - 276

Available online: 03 Apr 2018 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article