Journal of Economic, Finance Research and Review (JEFRR) · Research article · Open access
Comparing U.S. and Approaches to AI-Driven Fraud Prevention
Afari Ntiakoh, Christian Amoakoh, Deborah Akuele Apaflo, Yeboah Mary Magdalene
3 August 2026 · Vol. 2, Issue 8, pp. 485-489
DOI: 10.65150/EP-jefrr/V2E8/2026-02
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What is this article about?
The increasing sophistication of financial and cyber fraud has led national governments, banks and regulators globally to incorporate artificial intelligence (AI) into anti-fraud measures. This paper compares the reactive, decentralized approach adopted by the United States and more prescriptive, risk-based regulatory models embraced by global peers such as the European Union and the United Kingdom.
Article at a glance
- Published
- 3 August 2026
- Volume / issue / pages
- Vol. 2, Issue 8, pp. 485-489
- ISSN
- 3051-3650 (online) · 3051-3642 (print)
- Licence
- CC BY 4.0 — free to read and reuse with attribution
- Peer review
- Double-blind, at least two independent reviewers
- Publisher
- EP Journals Group
Abstract
The increasing sophistication of financial and cyber fraud has led national governments, banks and regulators globally to incorporate artificial intelligence (AI) into anti-fraud measures. This paper compares the reactive, decentralized approach adopted by the United States and more prescriptive, risk-based regulatory models embraced by global peers such as the European Union and the United Kingdom. By referencing academic studies, regulatory documents, and case studies of institutions, the paper covers how these tools (like machine learning (ML) models, biometric authentication, and real-time transaction monitoring) are used to detect and prevent fraudulent behavior, including identity theft and new generative-AI-driven scams. It also examines how different regulatory environments influence the adoption of AI and technology, with a focus on the intersection among innovation, compliance, privacy, and ethical governance. The results indicate that the U.S. model, with its flexibility and quick adaptability by sectors, can result in fractured oversight structures and breaks in compliance and accountability. In contrast, international approaches, including the EU’s AI Act and UK proposals, emphasize transparency, standardization, and risk reduction, but may restrict innovation through stringent regulatory demands. Effective AI-enabled fraud prevention demands common international standards, ethical AI governance, and enhanced cross-border data sharing mechanisms. It serves as a hero to transform global financial security and regulatory collaboration in the age of intelligent fraud detection.
Keywords: Fraud prevention, Artificial Intelligence, Data Privacy, Machine Learning, Regulatory Compliance.
Cite this article
Afari Ntiakoh, Christian Amoakoh, Deborah Akuele Apaflo, & Yeboah Mary Magdalene (2026). Comparing U.S. and Approaches to AI-Driven Fraud Prevention. Journal of Economic, Finance Research and Review, 2(8), 485-489. https://doi.org/10.65150/EP-jefrr/V2E8/2026-02
@article{Afari2026,
title = {Comparing U.S. and Approaches to AI-Driven Fraud Prevention},
author = {Afari Ntiakoh and Christian Amoakoh and Deborah Akuele Apaflo and Yeboah Mary Magdalene},
journal = {Journal of Economic, Finance Research and Review},
year = {2026},
volume = {2},
number = {8},
pages = {485-489},
doi = {10.65150/EP-jefrr/V2E8/2026-02},
url = {https://doi.org/10.65150/EP-jefrr/V2E8/2026-02}
}Questions about this article
Who wrote this article?
Afari Ntiakoh, Christian Amoakoh, Deborah Akuele Apaflo, Yeboah Mary Magdalene.
Where and when was it published?
In Journal of Economic, Finance Research and Review, Volume 2, Issue 8, pages 485-489, published by EP Journals Group on 3 August 2026.
Is this article free to read?
Yes. It is open access under a Creative Commons Attribution 4.0 (CC BY 4.0) licence, so anyone can read, share and reuse it with attribution.
What is the DOI of this article?
10.65150/EP-jefrr/V2E8/2026-02 (https://doi.org/10.65150/EP-jefrr/V2E8/2026-02).
How do I cite this article?
APA: Afari Ntiakoh, Christian Amoakoh, Deborah Akuele Apaflo, & Yeboah Mary Magdalene (2026). Comparing U.S. and Approaches to AI-Driven Fraud Prevention. Journal of Economic, Finance Research and Review, 2(8), 485-489. https://doi.org/10.65150/EP-jefrr/V2E8/2026-02
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