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Using adaptive learning in credit scoring to estimate take-up probability distribution

Record type: Article

Credit scoring is used by lenders to minimise the chance of taking an unprofitable account with the overall objective of maximising profit. Profit is generated when a good customer accepts an offer from the organisation. So it is also necessary to get the customers to accept the offer. A lender can ‘‘learn’’ about the customers preferences by looking at which type of product different types of customers accepted and hence has to decide what offer to make. In this model of the acceptance problem, we model the lenders decision problem on which offer to make as a Markov Decision Process under uncertainty. The aim of this paper is to develop a model of adaptive dynamic programming where Bayesian updating methods are employed to better estimate a take-up probability distribution. The significance of Bayesian updating in this model is that it allows previous responses to be included in the decision process. This means one uses learning of the previous responses to aid in selecting offers best to be offered to prospective customers that ensure take-up.

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Citation

Seow, Hsin-Vonn and Thomas, Lyn C. (2006) Using adaptive learning in credit scoring to estimate take-up probability distribution European Journal of Operational Research, 173, (3), pp. 880-892. (doi:10.1016/j.ejor.2005.06.058).

More information

Published date: 2006
Keywords: dynamic programming, bayesian updating, take-up probability, credit scoring

Identifiers

Local EPrints ID: 36773
URI: http://eprints.soton.ac.uk/id/eprint/36773
ISSN: 0377-2217
PURE UUID: aabfecd7-03e4-4a0b-9271-9fc80978c205

Catalogue record

Date deposited: 11 Jul 2006
Last modified: 17 Jul 2017 15:43

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Contributors

Author: Hsin-Vonn Seow
Author: Lyn C. Thomas

University divisions


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