Abstract

This study instantiates credit strategy optimization at the transaction authorization layer, with actions approve, review, and decline. Within an Offline Conservative RL (CQL) framework, we co-optimize fraud loss, operational burden from manual reviews, and customer friction from false positives and delays via a unified multi-objective cost function. Using a public credit-card transaction dataset with severe class imbalance, the learned policy improves total cost relative to cost-sensitive supervised baselines, while offering favorable trade-offs along a Pareto frontier between risk, operations, and friction. We detail the MDP design (state featurization, action space, and cost weights) and show that CQL mitigates out-of-distribution overestimation in offline settings. The results indicate that conservative RL is a practical path for transaction-level credit decision-making that balances fraud risk with operational efficiency and user impact.

Keywords

Offline Reinforcement LearningCost-Sensitive Credit Risk OptimizationUser-Centric Financial Decision SystemsConservative Q-Learning CQL

Publication

Accepted 2026 February 14 ; Published 2026 February 18

Journal of Economic Theory and Business Management, 2026, 3(1), 3006-4953

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Metadata

DOI

10.70393/6a6574626d.333932

ARK

ark:/40704/JETBM.v3n1a01

Pages

1–9

References

20

Disciplines

Business Analytics

Subjects

Econometric Modeling

Cite This Article

Acknowledgments

Not Applicable.

Funding

Not Applicable.

Institutional Review Board Statement

Not Applicable.

Data Availability Statement

Not Applicable.

Informed Consent Statement

Not Applicable.

Conflict of Interest

Not Applicable.

Author Contributions

Not application.

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Copyright © 2026 The Author(s). Published by Southern United Academy of Sciences.
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