Dynamic Credit-Collections Optimization

Naveed Chehrazi (), Peter W. Glynn () and Thomas Weber
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Naveed Chehrazi: Department of Information, Risk, and Operations Management, McCombs School of Business, The University of Texas at Austin, Austin, Texas 78705
Peter W. Glynn: Department of Management Science and Engineering, Stanford University, Stanford, California 94305

Management Science, 2019, vol. 67, issue 6, 2737-2769

Abstract: Based on a dynamic model of the stochastic repayment behavior exhibited by delinquent credit-card accounts in the form of a self-exciting point process, a bank can control the arrival intensity of repayments using costly account-treatment actions. A semi-analytic solution to the corresponding stochastic optimal control problem is obtained using a recursive approach. For a linear cost of treatment effort, the optimal policy in the two-dimensional (intensity, balance) space is described by the frontier of a convex action region. The unique optimal policy significantly reduces a bank’s loss given default and concentrates the collection effort onto the best possible actions at the best possible times so as to minimize the sum of the expected discounted outstanding balance and the discounted cost of the collection effort, thus maximizing the net value of any given delinquent credit-card account. This paper was accepted by Noah Gans, stochastic models and simulation.

Keywords: account valuation; consumer credit; credit collections; singular control; self-exciting point process; stochastic optimization; control of Hawkes processes (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (8)

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