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Monetary and relative scorecards to assess profits in consumer revolving credit

Author

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  • Luis Javier Sánchez Barrios

    (1] Universidad del Norte Business School, Barranquilla, Colombia[2] The University of Edinburgh Business School, Edinburgh, UK)

  • Galina Andreeva

    (The University of Edinburgh Business School, Edinburgh, UK)

  • Jake Ansell

    (The University of Edinburgh Business School, Edinburgh, UK)

Abstract

This paper presents for the first time a relative profit measure for scoring purposes and compares results with those obtained from monetary scores. The suggested measure is the cumulative profit relative to the outstanding debt. It can also be interpreted as the percentage coverage against default. Monetary and relative measures are compared with both being estimated using direct and indirect methods. Direct scores are obtained from borrower attributes, while indirect scores are predicted using the estimated probabilities of default and repurchase. Results show that specific segments of customers are profitable in both monetary and relative terms. The best performing indirect models use the probabilities of default within 12 months on books. This agrees with existing banking practice of default estimation. Direct models outperform indirect models. Relative scores would be preferred under more conservative standpoints towards default because of unstable conditions and if the aim is to penetrate relatively unknown segments. Further ethical considerations justify their use in an inclusive lending context.

Suggested Citation

  • Luis Javier Sánchez Barrios & Galina Andreeva & Jake Ansell, 2014. "Monetary and relative scorecards to assess profits in consumer revolving credit," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 443-453, March.
    • Handle: RePEc:pal:jorsoc:v:65:y:2014:i:3:p:443-453
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    Citations

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    Cited by:

    1. Jonathan Crook & David Edelman, 2014. "Special issue credit risk modelling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 321-322, March.
    2. Ahelegbey, Daniel Felix & Giudici, Paolo & Hadji-Misheva, Branka, 2019. "Latent factor models for credit scoring in P2P systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 112-121.
    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. Ahelegbey, Daniel & Giudici, Paolo & Pediroda, Valentino, 2023. "A network based fintech inclusion platform," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    5. Ahelegbey, Daniel Felix & Giudici, Paolo & Hadji-Misheva, Branka, 2019. "Factorial Network Models To Improve P2P Credit Risk Management," MPRA Paper 92633, University Library of Munich, Germany.
    6. 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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