Credit Scoring Models: Techniques and Issues

Yosi Lizar Eddy; Engku Muhammad Nazri Engku Abu Bakar

Credit Scoring Models: Techniques and Issues

Authors

Yosi Lizar Eddy Risk Quantification Section, Risk Management Department, Bank Muamalat Malaysia Berhad, 21 Jalan Melaka, 50100 Kuala Lumpur, Malaysia
Engku Muhammad Nazri Engku Abu Bakar Decision Science Department, School of Quantitative Sciences, Universiti Utara Malaysia, 06010 UUM Sintok, Kedah, Malaysia

Keywords:

DEMATEL, credit scoring model, logistic regression, analytic hierarchy process

Abstract

This paper presents a brief review on the current available techniques for credit scoring model, namely the statistical-based models and the artificial intelligence/machine learning- based models. It is then followed by the suggestions on how to revise the credit scoring model that is currently being adopted by any credit risk management, if revision is needed. The revision of the model involves the selection of criteria to be included as well as the weights to be given for the criteria. Some potential techniques in selecting the criteria and determining the weights for the selected criteria are also discussed.

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Published

2020-10-24

How to Cite

Lizar Eddy, Y., & Engku Abu Bakar, E. M. N. (2020). Credit Scoring Models: Techniques and Issues. Journal of Advanced Research in Business and Management Studies, 7(2), 29–41. Retrieved from https://akademiabaru.com/submit/index.php/arbms/article/view/1240