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A profit-based scoring system in consumer credit: making acquisition decisions for credit cards

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  • R T Stewart

    (Federal Reserve Bank of Chicago)

Abstract

Consumer credit scoring is one of the most successful applications of quantitative analysis in business with nearly every major lender using charge-off models to make decisions. Yet banks do not extend credit to control charge-off, but to secure profit. So, while charge-off models work well in rank-ordering the loan default costs associated with lending and are ubiquitous throughout the industry, the equivalent models on the revenue side are not being used despite the need. This paper outlines a profit-based scoring system for credit cards to be used for acquisition decisions by addressing three issues. First, the paper explains why credit card profit models—as opposed to cost or charge-off models—have been difficult to build and implement. Second, a methodology for modelling revenue on credit cards at application is proposed. Finally, acquisition strategies are explored that use both a spend model and a charge-off model to balance tradeoffs between charge-off, revenue, and volume.

Suggested Citation

  • R T Stewart, 2011. "A profit-based scoring system in consumer credit: making acquisition decisions for credit cards," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(9), pages 1719-1725, September.
  • Handle: RePEc:pal:jorsoc:v:62:y:2011:i:9:d:10.1057_jors.2010.135
    DOI: 10.1057/jors.2010.135
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    References listed on IDEAS

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    1. D. J. Hand & W. E. Henley, 1997. "Statistical Classification Methods in Consumer Credit Scoring: a Review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 523-541, September.
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    7. Andreeva, Galina & Ansell, Jake & Crook, Jonathan, 2007. "Modelling profitability using survival combination scores," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1537-1549, December.
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    Cited by:

    1. Lu Gao & Kanshukan Rajaratnam & Peter Beling, 2016. "Loan origination decisions using a multinomial scorecard," Annals of Operations Research, Springer, vol. 243(1), pages 199-210, August.
    2. Kaveh Bastani & Elham Asgari & Hamed Namavari, 2018. "Wide and Deep Learning for Peer-to-Peer Lending," Papers 1810.03466, arXiv.org, revised Oct 2018.
    3. Sanchez-Barrios, Luis Javier & Andreeva, Galina & Ansell, Jake, 2016. "“Time-to-profit scorecards for revolving credit”," European Journal of Operational Research, Elsevier, vol. 249(2), pages 397-406.
    4. Selcuk Bayraci, 2017. "Application of profit-based credit scoring models using R," Romanian Statistical Review, Romanian Statistical Review, vol. 65(4), pages 3-28, December.
    5. Baidoo, Edwin & Natarajan, Ramachandran, 2021. "Profit-based credit models with lender’s attitude towards risk and loss," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).

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