Abstract

The rapid digital transformation of healthcare systems has led to the emergence of highly interconnected healthcare ecosystems integrating electronic health records, telemedicine platforms, insurance systems, wearable devices, and cloud-based health information exchanges. While these advancements improve care delivery and operational efficiency, they also expand the attack surface for financial fraud, including billing manipulation, identity theft, insurance fraud, prescription abuse, and fraudulent claims processing. Artificial Intelligence (AI) has increasingly become a critical tool for detecting, predicting, and preventing fraud within these complex environments. This review paper examines recent advances in AI-based fraud analytics designed to strengthen financial protection across connected healthcare ecosystems. The study synthesizes developments in machine learning, deep learning, graph analytics, natural language processing, and hybrid anomaly detection models used to identify hidden fraud patterns in large-scale healthcare datasets. Particular attention is given to real-time analytics, explainable AI frameworks, federated learning approaches for privacy-preserving fraud detection, and integration with healthcare interoperability standards such as FHIR. The review also evaluates data governance challenges, algorithmic bias risks, model interpretability requirements, and regulatory compliance considerations affecting AI deployment in healthcare finance systems. Furthermore, the paper analyzes emerging architectures combining predictive analytics with automated compliance monitoring and intelligent auditing mechanisms. By comparing traditional rule-based fraud detection systems with adaptive AI-driven models, the study highlights measurable improvements in detection accuracy, scalability, and proactive risk mitigation. The findings demonstrate that AI-enabled fraud analytics can significantly enhance financial transparency, reduce revenue leakage, and support resilient healthcare infrastructures when aligned with ethical AI principles and secure data management practices. The paper concludes by identifying research gaps and proposing future directions for trustworthy, interoperable, and real-time fraud protection frameworks in digitally connected healthcare environments.