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.
Machine summary:
"Matrix Sequential Hybrid Credit Scorecard Based on Logistic Regression and Clustering Seyed Mahdi Sadatrasoul* Faculty of Management, Kharazmi University, Tehran, Iran (Received: August 31, 2017; Revised: December 26, 2017; Accepted: January 7, 2018) Abstract The Basel II Accord pointed out benefits of credit risk management through internal models to estimate Probability of Default (PD).
(View the image of this page)This paper looks forward to answering the key questions: "Can we build a model which can combine the profit and credit scoring attributes to make the final decision of lending for banks?" and " How can we better decide to lend money to grey applicants?".
There are many studies in the field of profit scoring, they mainly discussed that the development of profit scoring models is troublesome, because banks’ datasets usually lack data related to time and the loss of given defaults and profits from other bank services which are used by the customers including letter of guarantee, letter of credit, other transactional service fees which form the revenue of the banks (Lessmannet al.
(View the image of this page) Table 5 shows the results of seven-time selected clustering among 52 implementations, the number of clusters between 3 to 5 using Kohonen (KO), Two-Step (TS), and K-Means (KM) methods and in different clustering settings by feeding 32 variables to SPSS modeler 18.
(View the image of this page) Conclusions and Future Directions In this paper, a matrix sequential hybrid credit scorecard based on logistic regression and clustering is introduced."