Modelling the profitability of credit cards by Markov decision processes
Modelling the profitability of credit cards by Markov decision processes
This paper derives a Markov decision process model for the profitability of credit cards, which allows lenders to find an optimal dynamic credit limit policy. The states of the system are based on the borrower’s behavioural score and the decisions are what credit limit to give the borrower each period. In determining which Markov chain best describes the borrower’s performance, second order as well as first order Markov chains are considered and estimation procedures developed that deal with the low default levels that may exist in the data. A case study is given in which the optimal credit limit is derived and the results compared with the actual outcomes.
123-130
So, M.C.
c6922ccf-547b-485e-8b74-a9271e6225a2
Thomas, L.C.
a3ce3068-328b-4bce-889f-965b0b9d2362
1 July 2011
So, M.C.
c6922ccf-547b-485e-8b74-a9271e6225a2
Thomas, L.C.
a3ce3068-328b-4bce-889f-965b0b9d2362
So, M.C. and Thomas, L.C.
(2011)
Modelling the profitability of credit cards by Markov decision processes.
European Journal of Operational Research, 212 (1), .
(doi:10.1016/j.ejor.2011.01.023).
Abstract
This paper derives a Markov decision process model for the profitability of credit cards, which allows lenders to find an optimal dynamic credit limit policy. The states of the system are based on the borrower’s behavioural score and the decisions are what credit limit to give the borrower each period. In determining which Markov chain best describes the borrower’s performance, second order as well as first order Markov chains are considered and estimation procedures developed that deal with the low default levels that may exist in the data. A case study is given in which the optimal credit limit is derived and the results compared with the actual outcomes.
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Published date: 1 July 2011
Organisations:
Management, Southampton Business School
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Local EPrints ID: 177817
URI: http://eprints.soton.ac.uk/id/eprint/177817
ISSN: 0377-2217
PURE UUID: 1305b3b5-4467-4ae4-b56f-6928b1419c93
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Date deposited: 22 Mar 2011 15:24
Last modified: 14 Mar 2024 02:53
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Author:
L.C. Thomas
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