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Offline Deep Reinforcement Learning for Dynamic Pricing of Consumer Credit

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  • Raad Khraishi
  • Ramin Okhrati

Abstract

We introduce a method for pricing consumer credit using recent advances in offline deep reinforcement learning. This approach relies on a static dataset and requires no assumptions on the functional form of demand. Using both real and synthetic data on consumer credit applications, we demonstrate that our approach using the conservative Q-Learning algorithm is capable of learning an effective personalized pricing policy without any online interaction or price experimentation.

Suggested Citation

  • Raad Khraishi & Ramin Okhrati, 2022. "Offline Deep Reinforcement Learning for Dynamic Pricing of Consumer Credit," Papers 2203.03003, arXiv.org.
    • Handle: RePEc:arx:papers:2203.03003
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    References listed on IDEAS

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    1. Xi-liang Chen & Lei Cao & Chen-xi Li & Zhi-xiong Xu & Jun Lai, 2018. "Ensemble Network Architecture for Deep Reinforcement Learning," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-6, April.
    2. Rana, Rupal & Oliveira, Fernando S., 2014. "Real-time dynamic pricing in a non-stationary environment using model-free reinforcement learning," Omega, Elsevier, vol. 47(C), pages 116-126.
    3. Maxime C. Cohen & Ilan Lobel & Renato Paes Leme, 2020. "Feature-Based Dynamic Pricing," Management Science, INFORMS, vol. 66(11), pages 4921-4943, November.
    4. Lkhagvadorj Munkhdalai & Tsendsuren Munkhdalai & Oyun-Erdene Namsrai & Jong Yun Lee & Keun Ho Ryu, 2019. "An Empirical Comparison of Machine-Learning Methods on Bank Client Credit Assessments," Sustainability, MDPI, vol. 11(3), pages 1-23, January.
    5. Omar Besbes & Assaf Zeevi, 2009. "Dynamic Pricing Without Knowing the Demand Function: Risk Bounds and Near-Optimal Algorithms," Operations Research, INFORMS, vol. 57(6), pages 1407-1420, December.
    6. 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.
    7. Zhipeng Liang & Hao Chen & Junhao Zhu & Kangkang Jiang & Yanran Li, 2018. "Adversarial Deep Reinforcement Learning in Portfolio Management," Papers 1808.09940, arXiv.org, revised Nov 2018.
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    Cited by:

    1. 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.
    2. Tanut Treetanthiploet & Yufei Zhang & Lukasz Szpruch & Isaac Bowers-Barnard & Henrietta Ridley & James Hickey & Chris Pearce, 2023. "Insurance pricing on price comparison websites via reinforcement learning," Papers 2308.06935, arXiv.org.
    3. Anil Sharma & Freeman Chen & Jaesun Noh & Julio DeJesus & Mario Schlener, 2024. "Hedging and Pricing Structured Products Featuring Multiple Underlying Assets," Papers 2411.01121, arXiv.org.

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