AI-Powered Fraud Detection in Medicare Claims: Techniques and Analysis
Keywords:
Medicare Fraud, Machine Learning, Fraud Detection, Artificial Intelligence, Healthcare ClaimsAbstract
The implementation of AI-driven LLMs in the healthcare industry has had a profound effect and will continue to shape the healthcare and AI analytics sector. According to Straits research the healthcare analytics field is “valued at USD 17.61 billion in 2024 and is projected to reach USD 21.78 billion in 2025”. The rapidly shaping industry is just starting to grow. These implementations help increase cost effectiveness, implement fraud preventive measures, and risk reduction. The AI-driven implementation process of predictive analytics and finding patterns helps not only the healthcare industry but the beneficiaries indirectly. This study analyzes the use of supervised machine learning algorithms to detect fraudulent claims. This paper explains AI powered fraud Medicaid claims detection framework using machine learning algorithms applying to Medicare synthetic claims data set. Through Supervised Learning, focusing on random classification, along with explainable AI methods, this paper highlights how Medicare fraudulent claims are effectively found with high precision. In addition, this paper demonstrates important prerequisites such as Data preparation, model training, and evaluating performance. Our approach and results highlight the efficiency of AI in automating claims fraud detection, reducing manual laborers’ work and improving overall claims authentication processes. In addition, the paper also highlights statistical analysis and graphical representations that evaluate the efficacy of the generated model, contributing to real time issues with Medicare claims fraud.
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