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Developments in consumer credit risk assessment

Developments in consumer credit risk assessment
Developments in consumer credit risk assessment
Consumer credit risk assessment involves the use of risk assessment tools to manage a borrower’s account from the time of pre-screening a potential application through to the management of the account during its life and possible write-off. The riskiness of lending to a credit applicant is usually estimated using a logistic regression model though researchers have considered many other types of classifier and preliminary evidence suggest support vector machines seem to be the most accurate. The training of a classifier on a sample of accepted applicants rather than on a sample representative of the applicant population seems not to result in bias though it does result in difficulties in setting the cut off. Profit scoring is a promising line of research and the Basel 2 accord has had profound implications for the way in which credit applicants are assessed and bank policies adopted.
1356-3548
CRR-06-09
University of Southampton
Crook, J.N.
f2b1a121-eeb1-4f88-86d7-f96a197e8c94
Edelman, D.B.
9c382324-8d31-41fd-80dd-684b1d603a51
Thomas, L.C.
a3ce3068-328b-4bce-889f-965b0b9d2362
Crook, J.N.
f2b1a121-eeb1-4f88-86d7-f96a197e8c94
Edelman, D.B.
9c382324-8d31-41fd-80dd-684b1d603a51
Thomas, L.C.
a3ce3068-328b-4bce-889f-965b0b9d2362

Crook, J.N., Edelman, D.B. and Thomas, L.C. (2006) Developments in consumer credit risk assessment (Univeristy of Southampton Discussion Paper Series: Centre for Risk Research, CRR-06-09) Southampton, UK. University of Southampton 33pp.

Record type: Monograph (Discussion Paper)

Abstract

Consumer credit risk assessment involves the use of risk assessment tools to manage a borrower’s account from the time of pre-screening a potential application through to the management of the account during its life and possible write-off. The riskiness of lending to a credit applicant is usually estimated using a logistic regression model though researchers have considered many other types of classifier and preliminary evidence suggest support vector machines seem to be the most accurate. The training of a classifier on a sample of accepted applicants rather than on a sample representative of the applicant population seems not to result in bias though it does result in difficulties in setting the cut off. Profit scoring is a promising line of research and the Basel 2 accord has had profound implications for the way in which credit applicants are assessed and bank policies adopted.

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More information

Published date: 2006

Identifiers

Local EPrints ID: 58374
URI: http://eprints.soton.ac.uk/id/eprint/58374
ISSN: 1356-3548
PURE UUID: a0846d29-6cd1-45b4-8365-41b7db6f2e5e

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Date deposited: 15 Aug 2008
Last modified: 11 Dec 2021 17:56

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Contributors

Author: J.N. Crook
Author: D.B. Edelman
Author: L.C. Thomas

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