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eISSN: 2581-9615 || CODEN: WJARAI || Impact Factor 8.2 ||  CrossRef DOI

Research and review articles are invited for publication in March 2026 (Volume 29, Issue 3) Submit manuscript

Adaptive AI and quantum computing for real-time financial fraud detection and cyber-attack prevention in U.S. healthcare

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  • Adaptive AI and quantum computing for real-time financial fraud detection and cyber-attack prevention in U.S. healthcare

Alex Lwembawo Mukasa 1, *, Esther A. Makandah 2 and Sunday Anwansedo 3

1 Creospan, Chicago, United State of America.

2 The University of West Georgia, Department of Business Administration, Athens, Georgia, United State of America. 

3 Southern University A & M College, Department of Computer Science Baton Rouge, Louisiana Institute, United State of America.

Review Article

World Journal of Advanced Research and Reviews, 2025, 26(02), 2785-2794

Article DOI: 10.30574/wjarr.2025.26.2.1767

DOI url: https://doi.org/10.30574/wjarr.2025.26.2.1767

Received on 23 March 2025; revised on 09 May 2025; accepted on 11 May 2025

This article explores the integration of adaptive AI and quantum computing to combat financial fraud and cyber-attacks in the U.S. healthcare sector. By leveraging deep neural networks, reinforcement learning, and quantum-enhanced models, we propose a hybrid framework capable of achieving high fraud detection accuracy and anomaly detection in real-time. Case studies and empirical evaluations demonstrate the superiority of the framework over traditional methods, while ethical and regulatory implications are addressed to ensure responsible deployment.

Adaptive; Artificial Intelligence; Quantum; Healthcare

https://wjarr.com/sites/default/files/fulltext_pdf/WJARR-2025-1767.pdf

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Alex Lwembawo Mukasa, Esther A. Makandah and Sunday Anwansedo. Adaptive AI and quantum computing for real-time financial fraud detection and cyber-attack prevention in U.S. healthcare. World Journal of Advanced Research and Reviews, 2025, 26(2), 2785-2794. Article DOI: https://doi.org/10.30574/wjarr.2025.26.2.1767

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