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Justifying adverse actions with new scorecard technologies

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Author Info

  • Hand, David

    ()
    (Imperial College London)

  • Yu, Keming

    ()
    (Brunel University)

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    Abstract

    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.

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    Bibliographic Info

    Article provided by Capco Institute in its journal Journal of Financial Transformation.

    Volume (Year): 26 (2009)
    Issue (Month): ()
    Pages: 13-17

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    Handle: RePEc:ris:jofitr:1391

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    Web page: http://www.capco.com/

    Related research

    Keywords: Neural networks; credit scoring models;

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    Cited by:
    1. 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.
    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, 2010. "Economists’ hubris – the case of risk management," Journal of Financial Transformation, Capco Institute, vol. 28, pages 27-35.

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