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Managing credit lines and prices for bank one credit cards

Published: 01 September 2003 Publication History

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.

References

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Consumer Federation of America. 2001. Credit card issuers aggressively expand marketing and lines of credit on eve of new bankruptcy restrictions. Press release. Consumer Federation of America, Washington, DC, Feb. 27.
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Cited By

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  • (2022)Offline Deep Reinforcement Learning for Dynamic Pricing of Consumer CreditProceedings of the Third ACM International Conference on AI in Finance10.1145/3533271.3561682(325-333)Online publication date: 2-Nov-2022
  • (2019)Dynamic Credit-Collections OptimizationManagement Science10.1287/mnsc.2018.307065:6(2737-2769)Online publication date: 1-Jun-2019
  • (2011)Marketing Optimization in Retail BankingInterfaces10.1287/inte.1110.059741:5(485-505)Online publication date: 1-Sep-2011

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Published In

cover image Interfaces
Interfaces  Volume 33, Issue 5
Wagner prize papers
September 2003
101 pages
ISSN:0092-2102
EISSN:1526-551X
Issue’s Table of Contents

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INFORMS

Linthicum, MD, United States

Publication History

Published: 01 September 2003

Author Tags

  1. dynamic programming/optimal control: Markov
  2. dynamic programming/optimal control: finite state
  3. financial institutions: banks

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Cited By

View all
  • (2022)Offline Deep Reinforcement Learning for Dynamic Pricing of Consumer CreditProceedings of the Third ACM International Conference on AI in Finance10.1145/3533271.3561682(325-333)Online publication date: 2-Nov-2022
  • (2019)Dynamic Credit-Collections OptimizationManagement Science10.1287/mnsc.2018.307065:6(2737-2769)Online publication date: 1-Jun-2019
  • (2011)Marketing Optimization in Retail BankingInterfaces10.1287/inte.1110.059741:5(485-505)Online publication date: 1-Sep-2011

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