This article introduces a novel strategy for addressing health insurance fraud through the implementation of Graph-Based Social Network Analysis (SNA). By capitalizing on the interconnectedness of coverage holders, healthcare practitioners, and relevant entities, our approach generates an all-encompassing graph structure that encapsulates the complex network dynamics inherent in the health insurance ecosystem. An anomaly detection algorithm and the meticulous construction of node and edge attributes to identify minor patterns are employed to detect fraudulent activity. The outcomes illustrate the effectiveness of the suggested methodology and establish its superiority in comparison to the available alternatives. The contributions of the study to approaches for detecting health insurance fraud enhance the cost-effectiveness and integrity of the system. In light of the persistent gravity of health insurance fraud, this study offers practitioners and regulators in the industry a comprehensive and pertinent response.

As healthcare facilities digitize medical records of patients, particularly in the form of electronic health records (EHRs) and use revolutionary technologies like Artificial Intelligence (AI), it is critical to protect privacy, credibility, and availability of private data. Numerous benefits have been observed for employing AI in the healthcare sector, however, there also exist multiple risks. Medical experts have expressed concern about how AI can manage clinical and patient data without violating the Health Insurance Portability and Accountability Act (HIPAA). This paper investigates the complex association between AI, healthcare, and data privacy, focusing on legal frameworks, particularly HIPAA in the United States. The article first discusses HIPAA in detail along with several laws made under this act. It then delves into the gaps and inconsistencies of HIPAA and finally throws light on the potential role of AI itself in preserving data privacy in healthcare. It examines the impact of these legislations on medical professionals, technology vendors, as well as patients, emphasizing the importance of strong data regulatory structures and risk reduction efforts to prevent cybersecurity attacks and illicit data access.