Author(s)

Lai Soon Lee^1,2*, Ya Mei Tee¹, Hsin Vonn Seow³

¹Department of Mathematics, Faculty of Science, Universiti Putra Malaysia, Serdang, Malaysia.
²Laboratory of Computational Statistics and Operations Research, Institute for Mathematical Research, Universiti Putra Malaysia, Serdang, Malaysia.
³Nottingham University Business School, The University of Nottingham Malaysia Campus, Semenyih, Malaysia.

Banks have many variants of a product which they can offer to their customers. For example, a credit card can have different interest rates. So determining which variants of a product to offer to the new customers and having some indication on acceptance probability will aid with the profit optimisation for the banks. In this paper, the authors look at a model for maximisation of the profit looking at the past information via implementation of the dynamic programming model with elements of Bayesian updating. Numerical results are presented of multiple variants of a credit card product with the model providing the best offer for the maximum profit and acceptance probability. The product chosen is a credit card with different interest rates.

Credit Card, Credit Scoring, Dynamic Programming, Profitability

Lee, L. , Tee, Y. and Seow, H. (2017) Dynamic Programming for Estimating Acceptance Probability of Credit Card Products. Journal of Computer and Communications, 5, 56-75. doi: 10.4236/jcc.2017.514006.