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Optimal scoring cutoff policies and efficient frontiers

Author

Listed:
  • P Beling

    (University of Virginia)

  • Z Covaliu

    (Private Consultant)

  • R M Oliver

    (University of California)

Abstract

Multiple business objectives are increasingly important in determining account acquisition and management policies in scored retail credit and loan portfolios. These business objectives include profit and market share, as well as the more traditional management of risk. We formulate a mathematical model that addresses the problem of how acquisition decisions should be made with multiple, conflicting objectives when one, or more than one, scorecard is available to the portfolio manager. We show that iso-contours for expected profit, volume and loss are straight lines in the receiver operating characteristic (ROC) space and develop results that establish equivalence between ROC dominance, maximum expected profit, and efficient-frontier dominance in the space of multiple business measures. For two non-dominating scorecards, we derive the efficient frontiers in the profit-volume space and provide guidelines for choosing optimal policies based on the decision maker's trade-offs between objectives.

Suggested Citation

  • P Beling & Z Covaliu & R M Oliver, 2005. "Optimal scoring cutoff policies and efficient frontiers," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1016-1029, September.
    • Handle: RePEc:pal:jorsoc:v:56:y:2005:i:9:d:10.1057_palgrave.jors.2602021
    DOI: 10.1057/palgrave.jors.2602021
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    References listed on IDEAS

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    1. R M Oliver & E Wells, 2001. "Efficient frontier cutoff policies in credit portfolios," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(9), pages 1025-1033, September.
    2. 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.
    3. H Zhu & P A Beling & G A Overstreet, 2001. "A study in the combination of two consumer credit scores," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(9), pages 974-980, September.
    4. H Zhu & P A Beling & G A Overstreet, 2002. "A Bayesian framework for the combination of classifier outputs," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(7), pages 719-727, July.
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    Cited by:

    1. Xing, Jin & Chi, Guotai & Pan, Ancheng, 2024. "Instance-dependent misclassification cost-sensitive learning for default prediction," Research in International Business and Finance, Elsevier, vol. 69(C).
    2. S M Finlay, 2008. "Towards profitability: a utility approach to the credit scoring problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(7), pages 921-931, July.
    3. Kanshukan Rajaratnam & Peter Beling & George Overstreet, 2017. "Regulatory capital decisions in the Context of consumer loan portfolios," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(7), pages 847-858, July.
    4. K Rajaratnam & P Beling & G Overstreet, 2010. "Scoring decisions in the context of economic uncertainty," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 421-429, March.
    5. 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.
    6. Finlay, Steven, 2010. "Credit scoring for profitability objectives," European Journal of Operational Research, Elsevier, vol. 202(2), pages 528-537, April.
    7. 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.

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