Matrix Sequential Hybrid Credit Scorecard Based on Logistic Regression and Clustering

Document Type : Research Paper


Faculty of Management, Kharazmi University, Tehran, Iran


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.


Main Subjects

Baesens, B., Setiono, R., Mues, C., & Vanthienen, J. (2003). Using neural network rule extraction and decision tables for credit-risk evaluation. Management Science, 49(3), 312-329.
Ben-David, A. (2008). Rule effectiveness in rule-based systems: A credit scoring case study. Expert Systems with Applications, 34(4), 2783-2788.
Bonacchi, M., , Ferrari, M.,  Pellegrini, M., (2008), The lifetime value scorecard: From E-metrics to internet customer value, in Marc J. Epstein, Jean-François Manzoni (ed.)
Chi, B.-W., & Hsu, C.-C. (2012). A hybrid approach to integrate genetic algorithm into dual scoring model in enhancing the performance of credit scoring model. Expert Systems with Applications, 39(3), 2650-2661.
Crook, J. N., Edelman, D. B. , & Thomas, L. C.  (2007). Recent developments in consumer credit risk assessment. European Journal of Operational Research, 183(3), 1447-1465.
Dong, G., Lai, K. K., &Yen, J.  (2010). Credit scorecard based on logistic regression with random coefficients. Procedia Computer Science, 1(1), 2463-2468.
Eisenbeis, R. A. (1977). Pitfalls in the application of discriminant analysis in business, finance, and economics. The Journal of Finance, 32(3), 875-900.
Florez-Lopez, R. (2010). Effects of missing data in credit risk scoring: A comparative analysis of methods to achieve robustness in the absence of sufficient data. Journal of the Operational Research Society, 61(3), 486-501.
Hand, D. J. (2005). Good practice in retail credit scorecard assessment. Journal of the Operational Research Society, 56(9), 1109-1117.
Hand, D. J., & Adams, N. M. (2014). Selection bias in credit scorecard evaluation. Journal of the Operational Research Society, 65(3), 408-415.
Harrell, F. E., & Lee, K. L. (1985). A comparison of the discrimination of discriminant analysis and logistic regression under multivariate normality. In P. K. Sen (Ed.), Biostatistics: Statistics in Biomedical; Public Health; and Environmental Sciences (pp. 333343). The Bernard G. Greenberg Volume, New York: North-Holland.
Hoffmann, F., Baesens, B., Mues, C., Van Gestel, T., & Vanthienen, J. (2007). Inferring descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithms. European Journal of Operational Research, 177(1), 540-555.
Huang, Z., Chen, H., Hsu, C. J. , Chen, W. H., & Wu, S.  (2004). Credit rating analysis with support vector machines and neural networks: A market comparative study. Decision Support Systems, 37(4), 543-558.
Gao, L., Rajaratnam K., Beling P., (2015). Loan origination decisions using a multinomial scorecard, 243(02), 199–210
Koo, J.-Y., Park, C., & Jhun, M. (2009). A classification spline machine for building a credit scorecard. Journal of Statistical Computation and Simulation, 79(5), 681-689.
Lessmann, S., Baesens, B., Seow, H.-V., & Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. European Journal of Operational Research, 247(1), 124-136.
Malhotra, R., & Malhotra, D. K. (2002). Differentiating between good credits and bad credits using neuro-fuzzy systems. European Journal of Operational Research, 136(1), 190-211.
Martens, D., Baesens, B., Van Gestel, T. , &Vanthienen, J. (2007). Comprehensible credit scoring models using rule extraction from support vector machines. European Journal of Operational Research, 183(3), 1466-1476.
Ong, C. S., Huang, J. J., & Tzeng, G. H.  (2005). Building credit scoring models using genetic programming. Expert Systems with Applications, 29(1), 41-47.
Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied Mathematics, 20, 53-65.
Siddiqi, N. (2017). Intelligent credit scoring: Building and implementing better credit risk scorecards. New York: John Wiley & Sons.
Schreiner, M., Woller G., (2010). A Simple Poverty Scorecard for Nicaragua,
Thomas, L. C. (2009). Consumer credit models: Pricing, profit and portfolios., Oxford: Oxford University Press.
Van Gestel, T., & Baesens, B.  (2009). Credit risk management: Basic concepts: financial risk components, rating analysis, models, economic and regulatory capital. USA: Oxford University Press.
West, D. (2000). Neural network credit scoring models. Computers & Operations Research, 27(11), 1131-1152.
Wiginton, J. C. (1980). A note on the comparison of logit and discriminant models of consumer credit behavior. Journal of Financial and Quantitative Analysis, 15(03), 757-770.
Whittaker, J., Whitehead, C., and Somers. M., (2007).The Journal of the Operational Research Society. 58,( 7), 911-921.