AI-Driven Predictive Analytics for Fraud Detection in Healthcare: Developing a Proactive Approach to Identify and Prevent Fraudulent Activities

The financial stability and operational effectiveness of healthcare systems around the world are threatened by the widespread problem of healthcare fraud. Conventional fraud detection systems, which rely on human investigations and retroactive audits, are unable to adequately meet the complexity and expansion of modern fraud schemes which have evolved over the years. The aim of this research is to examine the potential of AI-driven predictive analytics in preventing healthcare fraud, focusing on the development of proactive initiatives to identify and prevent healthcare-related fraudulent activities. The findings indicate that in terms of accuracy, speed, and adaptability, AI-driven predictive analytics outperforms conventional fraud detection techniques Technologies such as NLP, supervised learning, and deep learning have proven successful in revealing hidden patterns in intricate datasets. Additionally, healthcare organizations can prioritize high-risk cases and react quickly to new threats by integrating real-time fraud detection systems with risk-scoring models. Therefore, AI-driven predictive analytics can proactively support healthcare fraud detection and prevention.