Time will tell: Behavioural Scoring and the Dynamics of Consumer Credit Assessment
AbstractThis paper discusses the use of dynamic modelling in consumer credit risk assessment. It surveys the approaches and objectives of behavioural scoring, customer scoring and profit scoring. It then investigates how Markov chain stochastic processes can be used to model the dynamics of the delinquency status and behavioural scores of consumers.
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Bibliographic InfoPaper provided by University of Southampton - Department of Accounting and Management Science in its series Papers with number 01-174.
Length: 32 pages
Date of creation: 2001
Date of revision:
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Postal: University of Southampton, Department of Accounting & Mangement Science, Southampton S09 5NH UK.
Phone: 44 0173 592537/592555
Fax: 44 0173 593858
Web page: http://www.soton.ac.uk/~econweb/
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Find related papers by JEL classification:
- C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
- D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory
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- Régis, Daniel E. & Artes, Rinaldo, 2008. "Modelo Multi-Estado de Markov em Cartões de Crédito," Insper Working Papers wpe_129, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
- So, Meko M.C. & Thomas, Lyn C., 2011. "Modelling the profitability of credit cards by Markov decision processes," European Journal of Operational Research, Elsevier, vol. 212(1), pages 123-130, July.
- G. Verstraeten & D. Van Den Poel, 2004. "The Impact of Sample Bias on Consumer Credit Scoring Performance and Profitability," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/232, Ghent University, Faculty of Economics and Business Administration.
- Bellotti, Tony & Crook, Jonathan, 2013. "Forecasting and stress testing credit card default using dynamic models," International Journal of Forecasting, Elsevier, vol. 29(4), pages 563-574.
- Finlay, Steven, 2011. "Multiple classifier architectures and their application to credit risk assessment," European Journal of Operational Research, Elsevier, vol. 210(2), pages 368-378, April.
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