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

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), 880-892. (doi:10.1016/j.ejor.2005.06.058).


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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.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1016/j.ejor.2005.06.058
ISSNs: 0377-2217 (print)
Related URLs:
Keywords: dynamic programming, bayesian updating, take-up probability, credit scoring
Subjects: H Social Sciences > HG Finance
H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Divisions : University Structure - Pre August 2011 > School of Management
ePrint ID: 36773
Accepted Date and Publication Date:
Date Deposited: 11 Jul 2006
Last Modified: 31 Mar 2016 12:05

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