Matrix Sequential Hybrid Credit Scorecard Based on Logistic Regression and Clustering
Publish place: Iranian Journal of Management Studies، Vol: 11، Issue: 1
Publish Year: 1397
Type: Journal paper
Language: English
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Document National Code:
JR_JIJMS-11-1_005
Index date: 28 August 2023
Matrix Sequential Hybrid Credit Scorecard Based on Logistic Regression and Clustering abstract
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.
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Matrix Sequential Hybrid Credit Scorecard Based on Logistic Regression and Clustering authors
سیدمهدی سادات رسول
Faculty of Management, Kharazmi University, Tehran, Iran
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