Reinforcement Learning for Prioritizing Anti-Money Laundering Case Reviews Based on Dynamic Risk Assessment

Luqing Ren

doi:10.70393/6a6574626d.333231

OPEN ACCESS |Research Article ||18 October 2025

Reinforcement Learning for Prioritizing Anti-Money Laundering Case Reviews Based on Dynamic Risk Assessment

Luqing Ren

Columbia University

lr3130@columbia.edu

Columbia University, New York, USA.

* Corresponding Author¹: Luqing Ren, E-Mail: lr3130@columbia.edu

Publication

Accepted 2025 September 29 ; Published 2025 October 18

Journal of Economic Theory and Business Management, 2025, 2(5), 3006-4953.

Abstract

Addressing the challenges of delayed risk assessment and inflexible prioritization strategies in anti-money laundering reviews, this study investigates reinforcement learning approaches for generating dynamic risk-driven prioritization strategies. It details state-action modeling, reward function design, and policy network training methods, while outlining the model's integration into operational workflows. An experimental environment was constructed using real financial review data. Results demonstrate the model's significant advantages in review efficiency and high-risk identification accuracy, showcasing its potential for online deployment and continuous optimization.

Keywords

Anti-Money Laundering , Reinforcement Learning , Dynamic Risk Assessment , Priority Ranking .

Metadata

DOI:

10.70393/6a6574626d.333231

ARK:

ark:/40704/JETBM.v2n5a01

Pages: 1-6

References: 7

Disciplines: Finance

Subjects: Corporate Finance

Cite This Article

APA Style

Ren, L. (2025). Reinforcement learning for prioritizing anti-money laundering case reviews based on dynamic risk assessment. Journal of Economic Theory and Business Management, 2(5), 1-6. https://doi.org/10.70393/6a6574626d.333231

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

References

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PUBLISHER'S NOTE

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Copyright © 2025 The Author(s). Published by Southern United Academy of Sciences.
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