Decision Support System for Credit Risk Management: An Empirical Study

Decision Support System for Credit Risk Management: An Empirical Study

Mehmet Resul Bilginci (Yapı Kredi Bank, Istanbul, Turkey), Gamze Ogcu Kaya (Sampoerna University, Jakarta, Indonesia) and Ali Turkyilmaz (Nazarbayev University, Astana, Kazakhstan)
Copyright: © 2019 |Pages: 14
DOI: 10.4018/IJISSS.2019040102

Abstract

Risk is an integrated part of the banking functions, which cannot be eliminated completely but it can be reduced by employing appropriate techniques. Credit processing is one of the core functions in the banking system, and its performance is closely related to management of the risks. The aim of this article is to develop a credit scorecard model which can be used as decision support system. A logistic regression with stepwise selection method is used to estimate the model parameters. The data that is used to construct the credit scorecard model is obtained from one of the pioneering banks in Turkish Banking Sector. The performance of the developed model is tested using statistical metrics including Receiver Operator Characteristic (ROC) curve and Gini statistics. The result reveals that the model performs well and it can be used as a decision support system for managing the credit risk by managers of the banks.
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2. Literature Review

Quality of bank’s loan portfolio is closely associated with the competitiveness and profitability of the bank. Quality of bank loan increases as the number of customers having high creditworthiness. Credit scoring is the most important decision support system which is utilized in evaluating the creditworthiness of the applicant. Therefore, it is possible to define the credit scoring as the process of modeling the creditworthiness of an applicant (Crook, Edelman and Thomas, 2007).

There are different definitions for credit scoring in the literature. In Anderson (2007), it is defined as the transformation of relevant credit factors into numeric measures by using statistical methods to be utilized as a guide in giving credit decision. In Malhotra & Malhotra (2003), it is described as an analytical model having constructed empirically based on past applications data used to predict credit-worthiness of applicant by utilization of probability of default.

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