Medical Insurance Fraud Detection Using Machine Learning

Abstract

Medical insurance fraud poses significant challenges to the healthcare industry, impacting financial resources and patient care. This research explores the application of machine learning methodologies to detect fraudulent activities within healthcare insurance claims. Medical insurance fraud detection is crucial to help insurance companies save money. Machine learning is a powerful tool that can be used to detect fraudulent activities in the healthcare industry. Fraud can be spread broadly and extremely costly to the therapeutic protection framework. Protection can be made unscrupulous and be a case designed to hide or alter such information meant for social insurance benefits. Cheats might be numerous and submitted by the protection guarantor or the safeguarded. The unscrupulous social insurance providers are the reason for extortion in the well-being segment.

This research approach is to apply machine learning to find incidents of medical insurance fraud automatically. In conclusion, machine learning is a promising tool for detecting medical insurance fraud. It can help insurers detect fraudulent activities in real time, saving money on bogus claims.