Khraishi, R;
Okhrati, R;
(2022)
Offline Deep Reinforcement Learning for Dynamic Pricing of Consumer Credit.
In: Magazzeni, D and Kumar, S and Savani, R and Xu, R and Ventre, C and Horvath, B and Hu, R and Balch, T and Toni, F, (eds.)
ICAIF '22: Proceedings of the Third ACM International Conference on AI in Finance.
(pp. pp. 325-333).
Association for Computing Machinery (ACM): New York, NY, USA.
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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 as opposed to commonly used pricing approaches it 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. In particular, using historical data on online auto loan applications we estimate an increase in expected profit of 21% with a less than 15% average change in prices relative to the original pricing policy.
| Type: | Proceedings paper |
|---|---|
| Title: | Offline Deep Reinforcement Learning for Dynamic Pricing of Consumer Credit |
| Event: | Third ACM International Conference on AI in Finance (ICAIF '22) |
| ISBN-13: | 9781450393768 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3533271.3561682 |
| Publisher version: | https://doi.org/10.1145/3533271.3561682 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | pricing, consumer credit, revenue management, finance, reinforcement learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10163994 |
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