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

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 lender's 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.