aceNext-Generation Machine Learning in Healthcare Fraud Detection: Current Trends, Challenges, and Future Research Directions
The growing complexity and size of healthcare systems have rendered fraud detection increasingly challenging; however, the current literature lacks a holistic view of the latest machine learning (ML) techniques with practical implementation concerns. The present study addresses this gap by highlighting the importance of machine learning (ML) in preventing and mitigating healthcare fraud, evaluating recent advancements, investigating implementation barriers, and exploring future research dimensions. To further ad…
Machine learning approaches including supervised, unsupervised, deep learning and hybrid methods improve prevention and detection of healthcare fraud in large, imbalanced datasets.
Employing machine learning for healthcare fraud detection is limited by poor data quality, scalability issues, regulatory compliance requirements, and resource constraints.
Real-world deployment is constrained by data quality, system scalability, regulatory compliance, and resource constraints in healthcare systems.
Evidence
- Peer-reviewedInformation2025-08-25
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Truvace Impact Record TRV-2026-0494, v1: “Next-Generation Machine Learning in Healthcare Fraud Detection: Current Trends, Challenges, and Future Research Directions.” Truvace, 2026-07-22. /record/TRV-2026-0494 (accessed at citation time). sha256 21fbe3a73158621a…
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