Machine Learning in Medicare Fraud Detection: Safeguarding Public Resources

Fraud in receipt and provision of Medicare is one of the most dangerous threats to public healthcare delivery systems, wasting billions of dollars annually and distorting the foundations upon which healthcare solutions are based. Conventional approaches to identifying fraud have become ineffective owing to the new and complex techniques undertaken by fraudsters. Through this paper, an effort is made to discuss the role of ML in identifying and combating Medicare fraud, specifically to preserve public assets. Using supervised learning, unsupervised learning, and deep learning are promising methods to detect patterns that are possibly related to fraud activities. Applying these techniques can help analyze a huge amount of data, learn from precedents, and identify elaborate and sophisticated trends that are hardly discernable using traditional approaches. This paper will provide an extensive investigation of various ML approaches to Medicare fraud detection. At this step, we experimentally analyze the most popular and effective ones, like decision trees, random forests, SVM, creative neural networks, and clusters. From the results obtained, it is clear that these advanced ML techniques can enhance the performance of fraud detection methods by dramatically minimizing false positives and enhancing the early detection of fraudsters in claims processing. In addition, the ethical concerns, future prospects, and difficulties of employing ML in this particular field. The use of machine learning in Medicare fraud prevention and identification mechanisms to prevent fraud greatly has the potential to transform the protection of public resources, which is crucial to ensuring that healthcare funds are used optimally.