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Recent developments in Consumer Credit Risk assessment

Recent developments in Consumer Credit Risk assessment
Recent 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 whilst preliminary evidence suggest support vector machines seem to be the most accurate, data quality issues may prevent these laboratory based results from being achieved in practice. 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.
finance, OR in banking, risk analysis
0377-2217
1447-1465
Crook, Jonathan N.
ce664ca4-e43a-4239-95c5-e358d311aa72
Edelman, David B.
f7e1a250-6920-4a3b-94b8-44056e962512
Thomas, Lyn C.
a3ce3068-328b-4bce-889f-965b0b9d2362
Crook, Jonathan N.
ce664ca4-e43a-4239-95c5-e358d311aa72
Edelman, David B.
f7e1a250-6920-4a3b-94b8-44056e962512
Thomas, Lyn C.
a3ce3068-328b-4bce-889f-965b0b9d2362

Crook, Jonathan N., Edelman, David B. and Thomas, Lyn C. (2007) Recent developments in Consumer Credit Risk assessment. European Journal of Operational Research, 183 (3), 1447-1465. (doi:10.1016/j.ejor.2006.09.100).

Record type: Article

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 whilst preliminary evidence suggest support vector machines seem to be the most accurate, data quality issues may prevent these laboratory based results from being achieved in practice. 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: 2007
Keywords: finance, OR in banking, risk analysis

Identifiers

Local EPrints ID: 51326
URI: http://eprints.soton.ac.uk/id/eprint/51326
ISSN: 0377-2217
PURE UUID: dae2d027-24fd-454f-9d8c-371bb52393ae

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Date deposited: 30 May 2008
Last modified: 15 Mar 2024 10:17

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Contributors

Author: Jonathan N. Crook
Author: David B. Edelman
Author: Lyn C. Thomas

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