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Justifying adverse actions with new scorecard technologies




It has been argued that flexible classification models such as neural networks, support vector machines, and random forests face resistance as credit scoring models because it is difficult to identify which characteristics contribute substantially to the overall scores. In fact, however, this is a misunderstanding arising from the fact that standard models are based on sums of transformations of the raw characteristics. We distinguish between the need to identify which characteristics contribute most to an individual‟s score and the need to identify which characteristics contribute to the performance of a scorecard. We describe solutions to these two problems, and illustrate by applying a range of scorecard approaches to some real credit card data.

Suggested Citation

  • Hand, David & Yu, Keming, 2009. "Justifying adverse actions with new scorecard technologies," Journal of Financial Transformation, Capco Institute, vol. 26, pages 13-17.
  • Handle: RePEc:ris:jofitr:1391

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    References listed on IDEAS

    1. Edward I. Altman & Brooks Brady & Andrea Resti & Andrea Sironi, 2005. "The Link between Default and Recovery Rates: Theory, Empirical Evidence, and Implications," The Journal of Business, University of Chicago Press, vol. 78(6), pages 2203-2228, November.
    2. Dvorak, Tomas & Podpiera, Richard, 2006. "European Union enlargement and equity markets in accession countries," Emerging Markets Review, Elsevier, vol. 7(2), pages 129-146, June.
    3. Egert, Balazs & Kocenda, Evzen, 2007. "Interdependence between Eastern and Western European stock markets: Evidence from intraday data," Economic Systems, Elsevier, vol. 31(2), pages 184-203, June.
    4. Brian L. Betker, 1997. "The Administrative Costs of Debt Restructurings: Some Recent Evidence," Financial Management, Financial Management Association, vol. 26(4), Winter.
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    Cited by:

    1. Shojai, Shahin & Feiger, George, 2010. "Economists’ hubris – the case of risk management," Journal of Financial Transformation, Capco Institute, vol. 28, pages 27-35.
    2. Shojai, Shahin & Feiger, George & Kumar, Rajesh, 2010. "Economists’ hubris — the case of equity asset management," Journal of Financial Transformation, Capco Institute, vol. 29, pages 9-16.
    3. Shojai, Shahin & Feiger, George, 2011. "Economists’ Hubris – The Case of Award Winning Finance Literature," Journal of Financial Transformation, Capco Institute, vol. 31, pages 9-17.

    More about this item


    Neural networks; credit scoring models;

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill


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