Justifying adverse actions with new scorecard technologies

David Hand () and Keming Yu ()
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David Hand: Imperial College London, Postal: South Kensington Campus, London SW7 2AZ, http://www3.imperial.ac.uk/people/d.j.hand
Keming Yu: Brunel University, Postal: Mathematical Sciences , John Crank 209, Uxbridge , UB8 3PH , United Kingdom, http://www.brunel.ac.uk/about/acad/siscm/maths/people/acad/kemingyu

Journal of Financial Transformation, 2009, vol. 26, 13-17

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.

Keywords: Neural networks; credit scoring models (search for similar items in EconPapers)
JEL-codes: G21 G32 (search for similar items in EconPapers)
Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:ris:jofitr:1391

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