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Managing Credit Lines and Prices for Bank One Credit Cards

Author

Listed:
  • Margaret S. Trench

    (Bank One Card Services, Inc., 3 Christina Centre, Wilmington, Delaware 19801)

  • Shane P. Pederson

    (Bank One Card Services, Inc., 2500 Westfield Drive, Elgin, Illinois 60123)

  • Edward T. Lau

    (Bank One Card Services, Inc., 3 Christina Centre, Wilmington, Delaware 19801)

  • Lizhi Ma

    (Bank One Card Services, Inc., 3 Christina Centre, Wilmington, Delaware 19801)

  • Hui Wang

    (Bank One Card Services, Inc., 3 Christina Centre, Wilmington, Delaware 19801)

  • Suresh K. Nair

    (School of Business, University of Connecticut, Storrs, Connecticut 06269)

Abstract

We developed a method for managing the characteristics of a bank's card holder portfolio in an optimal manner. The annual percentage rate (APR) and credit line of an account influence card use and bank profitability. Consumers find low APRs and high credit lines attractive. However, low APRs may reduce bank profitability, while indiscriminate increases in credit lines increase the bank's exposure to credit loss. We designed the PORTICO (portfolio control and optimization) system using Markov decision processes (MDP) to select price points and credit lines for each card holder that maximize net present value (NPV) for the portfolio. PORTICO uses account-level historical information on purchases, payments, profitability, and delinquency risk to determine pricing and credit-line changes. In competitive benchmark tests over more than a year, the PORTICO model outperforms the bank's current method and may increase annual profits by over $75 million.

Suggested Citation

  • Margaret S. Trench & Shane P. Pederson & Edward T. Lau & Lizhi Ma & Hui Wang & Suresh K. Nair, 2003. "Managing Credit Lines and Prices for Bank One Credit Cards," Interfaces, INFORMS, vol. 33(5), pages 4-21, October.
    • Handle: RePEc:inm:orinte:v:33:y:2003:i:5:p:4-21
    DOI: 10.1287/inte.33.5.4.19245
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Lapshin, Viktor & Anton, Markov, 2022. "MCMC-based credit rating aggregation algorithm to tackle data insufficiency," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 68, pages 50-72.
    2. Jason R. W. Merrick & Jill R. Hardin & Russell Walker, 2006. "Partnerships in Training," Interfaces, INFORMS, vol. 36(4), pages 359-370, August.
    3. Naveed Chehrazi & Peter W. Glynn & Thomas A. Weber, 2019. "Dynamic Credit-Collections Optimization," Management Science, INFORMS, vol. 67(6), pages 2737-2769, June.
    4. Malik, Madhur & Thomas, Lyn C., 2012. "Transition matrix models of consumer credit ratings," International Journal of Forecasting, Elsevier, vol. 28(1), pages 261-272.
    5. Conor B. Hamill & Raad Khraishi & Simona Gherghel & Jerrard Lawrence & Salvatore Mercuri & Ramin Okhrati & Greig A. Cowan, 2023. "Agent-based Modelling of Credit Card Promotions," Papers 2311.01901, arXiv.org, revised Nov 2023.
    6. So, Meko M.C. & Thomas, Lyn C., 2011. "Modelling the profitability of credit cards by Markov decision processes," European Journal of Operational Research, Elsevier, vol. 212(1), pages 123-130, July.
    7. Raad Khraishi & Ramin Okhrati, 2022. "Offline Deep Reinforcement Learning for Dynamic Pricing of Consumer Credit," Papers 2203.03003, arXiv.org.
    8. He, Ping & Hua, Zhongsheng & Liu, Zhixin, 2015. "A quantification method for the collection effect on consumer term loans," Journal of Banking & Finance, Elsevier, vol. 57(C), pages 17-26.
    9. Suting Hong & Robert M. Hunt & Konstantinos Serfes, 2023. "Dynamic Pricing of Credit Cards and the Effects of Regulation," Journal of Financial Services Research, Springer;Western Finance Association, vol. 64(1), pages 81-131, August.
    10. Mohammad G Nejad & Sertan Kabadayi, 2016. "Optimal introductory pricing for new financial services," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 21(1), pages 34-50, March.
    11. Jonathan Crook & Tony Bellotti, 2010. "Time varying and dynamic models for default risk in consumer loans," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(2), pages 283-305, April.
    12. Zhixin Liu & Ping He & Bo Chen, 2019. "A Markov decision model for consumer term-loan collections," Review of Quantitative Finance and Accounting, Springer, vol. 52(4), pages 1043-1064, May.
    13. Crook, Jonathan N. & Edelman, David B. & Thomas, Lyn C., 2007. "Recent developments in consumer credit risk assessment," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1447-1465, December.
    14. Ramasubramanian Sundararajan & Tarun Bhaskar & Abhinanda Sarkar & Sridhar Dasaratha & Debasis Bal & Jayanth K. Marasanapalle & Beata Zmudzka & Karolina Bak, 2011. "Marketing Optimization in Retail Banking," Interfaces, INFORMS, vol. 41(5), pages 485-505, October.

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