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A Robust Credit Screening Model Using Categorical Data

Author

Listed:
  • Peter Kolesar

    (Graduate School of Business, Columbia University, New York, New York 10027)

  • Janet L. Showers

    (Salomon Brothers Inc, 1 New York Plaza, New York, New York 10004)

Abstract

Motivated by an application in a public utility, the credit screening problem is re-examined from a decision theoretic viewpoint. The relationships between several alternative problem formulations are explored, and compared to the classical linear discriminant analysis (LDA) approach. Several mathematical programming based solution methods are proposed when the data are binary, and an efficient algorithm is developed for the case when the screening function must also have binary weights. Actual results of both the mathematical programming and LDA methods are presented and compared. The resulting mathematical programming rules are effective, robust, and flexible to administer. Practical advantages of the resulting "n out of N" type rules are discussed. These screening rules have been widely implemented by a major public utility and have resulted in substantial benefits to the utility and to the public.

Suggested Citation

  • Peter Kolesar & Janet L. Showers, 1985. "A Robust Credit Screening Model Using Categorical Data," Management Science, INFORMS, vol. 31(2), pages 123-133, February.
    • Handle: RePEc:inm:ormnsc:v:31:y:1985:i:2:p:123-133
    DOI: 10.1287/mnsc.31.2.123
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    Citations

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    Cited by:

    1. Mark Schreiner, 2015. "A Comparison of Two Simple, Low-Cost Ways for Local, Pro-Poor Organizations to Measure the Poverty of Their Participants," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 124(2), pages 537-569, November.
    2. Thomas, Lyn C., 2000. "A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers," International Journal of Forecasting, Elsevier, vol. 16(2), pages 149-172.
    3. Seifert, Daniel & Seifert, Ralf W. & Protopappa-Sieke, Margarita, 2013. "A review of trade credit literature: Opportunities for research in operations," European Journal of Operational Research, Elsevier, vol. 231(2), pages 245-256.
    4. Fernandes, Guilherme Barreto & Artes, Rinaldo, 2016. "Spatial dependence in credit risk and its improvement in credit scoring," European Journal of Operational Research, Elsevier, vol. 249(2), pages 517-524.
    5. Fernandes, Guilherme Barreto & Artes , Rinaldo, 2013. "Spatial correlation in credit risk and its improvement in credit scoring," Insper Working Papers wpe_321, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    6. Mark Schreiner & Michal Matul & Ewa Pawlak & Sean Kline, 2014. "Poverty Scorecards: Lessons from a Microlender in Bosnia‐Herzegovina," Poverty & Public Policy, John Wiley & Sons, vol. 6(4), pages 407-428, December.
    7. Rayo Cantón, Salvador & Lara Rubio, Juan & Camino Blasco, David, 2010. "A Credit Scoring Model For Institutions Of Microfinance Under The Basel Ii Normative," Journal of Economics, Finance and Administrative Science, Universidad ESAN, vol. 15(28), pages 89-124.

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