How to measure the quality of credit scoring models

Finance a Uver - Czech Journal of Economics and Finance

Volumn 61, Issue 5, 2011, Pages 486-507

  Řezáč, Martin ^a   Řezáč, František ^a  

^a MASARYK UNIVERSITY   (Czech Republic)

Author keywords

Credit scoring; Lift; Normally distributed scores; Profit; Quality indices

Indexed keywords

EID: 80355136722     PISSN: 00151920     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Review

References (30)

1. Anderson R (2007): The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation. Oxford, Oxford University Press.
2. Berry MJA, Linoff GS (2004): Data Mining Techniques: for Marketing, Sales, and Customer Relationship Management. 2nd ed. Indianapolis, Wiley.
3. Coppock DS (2002): Why Lift? DM Review Online. www.dmreview.com/news/5329-1.html. Accessed on December 1, 2009.
4. Crook JN, Edelman DB, Thomas LC (2007): Recent developments in consumer credit risk assessment. European Journal of Operational Research, 183(3):1447-1465.
5. Engelmann B, Hayden E, Tasche D (2003): Measuring the Discriminatory Power of Rating System. http://www.bundesbank.de/download/bankenaufsicht/dkp/200301dkp_b.pdf. Accessed on October 4, 2010.
6. Giudici P (2003): Applied Data Mining: statistical methods for business and industry. Chichester, Wiley.
7. Hand DJ, Henley WE (1997): Statistical Classification Methods in Consumer Credit Scoring: a review. Journal. of the Royal Statistical Society, Series A, 160(3):523-541.
8. Harrell FE, Lee KL, Mark DB (1996): Multivariate prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in Medicine, 15:361-387.
9. Hřebíček J, Řezáč M (2008): Modelling with Maple and MapleSim. In: 22nd European Conference on Modelling nad Simulation ECMS 2008 Proceedings, Dudweiler, pp. 60-66.
10. Kočenda E, Vojtek M (2011): Default Predictors in Retail Credit Scoring: Evidence from Czech Banking Data. Forthcoming in: Emerging Markets Finance and Trade.
11. Koláček J, Řezáč M (2010): Assessment of Scoring Models Using Information Value. In: Keynote, Invited and Contributed Papers. 19th International Conference on Computational Statistics, Paris, SpringerLink, pp. 1191-1198.
12. Lilliefors HW (1967): On the Komogorov-Smirnov test for normality with mean and variance unknown. Journal of the American Statistical Association, 62:399-402.
13. Müller M, Rönz B (2000): Credit Scoring using Semiparametric Methods. In: Franke J, Härdle W, Stahl G (Eds.): Measuring Risk in Complex Stochastic Systems. New York, Springer-Verlag.
14. Nelsen RB (1998): Concordance and Gini's measure of association. Journal of Nonparametric Statistics, 9(3):227-238.
15. Newson R (2006): Confidence intervals for rank statistics: Somers' D and extensions. The Stata Journal, 6(3):309-334.
16. Řezáč M (2003): Maximal Smoothing. Journal of Electrical Engineering, 54:44-46.
17. Řezáč M (2011): Advanced Empirical Estimate of Information Value for Credit Scoring Models. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, LIX(2):267-273.
18. Řezáč M, Řezáč F (2009): Measuring the Quality of Credit Scoring Models. http://www.crc.man.ed.ac.uk/conference/archive/2009/presentations/Paper-65-Paper.pdf. Accessed on October 4, 2010.
19. Siddiqi N (2006): Credit Risk Scorecards: developing and implementing intelligent credit scoring. New Jersey, Wiley.
20. Sobehart J, Keenan S, Stein R (2000): Benchmarking Quantitative Default Risk Models: A Validation Methodology, Moody's Investors Service. http://www.algorithmics.com/EN/media/pdfs/Algo-RA0301-ARQ-DefaultRiskModels.pdf. Accessed on October 4, 2010.
21. Somers RH (1962): A new asymmetric measure of association for ordinal variables. American Sociological Review, 27:799-811.
22. Terrell GR (1990): The Maximal Smoothing Principle in Density Estimation. Journal of the American Statistical Association, 85:470-477.
23. Thomas LC (2000): A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers. International Journal of Forecasting, 16(2):149-172.
24. Thomas LC (2009): Consumer Credit Models: Pricing, Profit, and Portfolio. Oxford, Oxford University Press.
25. Thomas LC, Edelman DB, Crook JN (2002): Credit Scoring and Its Applications. Philadelphia, SIAM Monographs on Mathematical Modeling and Computation.
26. Vojtek M, Kočenda E (2006): Credit Scoring Methods. Finance a úvěr-Czech Journal of Economics and Finance, 56(3-4):152-167.
27. Wand MP, Jones MC (1995): Kernel Smoothing. London, Chapman and Hall.
28. Wilkie AD (2004): Measures for comparing scoring systems. In: Thomas LC, Edelman DB, Crook JN (Eds.): Readings in Credit Scoring. Oxford, Oxford University Press.
29. Witzany J (2009): Definition of Default and Quality of Scoring Functions. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1467718. Accessed on September 1, 2010.
30. Xu K (2003): How has the literature on Gini's index evolved in past 80 years? http://economics.dal.ca/RePEc/dal/wparch/howgini.pdf. Accessed on December 1, 2009.