Matrix Sequential Hybrid Credit Scorecard Based on Logistic Regression and Clustering | ||
| Interdisciplinary Journal of Management Studies | ||
| مقاله 5، دوره 11، شماره 1، بهار 2018، صفحه 91-111 اصل مقاله (365.24 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/ijms.2018.242718.672842 | ||
| نویسنده | ||
| Seyed Mahdi Sadatrasoul* | ||
| Faculty of Management, Kharazmi University, Tehran, Iran | ||
| چکیده | ||
| The Basel II Accord pointed out benefits of credit risk management through internal models to estimate Probability of Default (PD). Banks use default predictions to estimate the loan applicants’ PD. However, in practice, PD is not useful and banks applied credit scorecards for their decision making process. Also the competitive pressures in lending industry forced banks to use profit scorecards, which show the profitability of customers. Applying these scorecards together makes the loan decision making process for banks more confusing. This paper has an obvious and clean solution for facilitating the confusion of loan decision making process by combining the credit and profit scorecards through introducing a matrix sequential hybrid credit scorecard. The applicability of the introduced matrix sequential hybrid scorecard results are shown using data from an Iranian bank. | ||
| کلیدواژهها | ||
| Credit scoring؛ Banking Industry؛ credit scorecard؛ profit scoring؛ matrix scorecard | ||
| مراجع | ||
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