Machine Learning Model for Fraud Detection in Healthcare

This technical report outlines the implementation of a machine learning (ML) model designed to detect fraud, waste, and abuse (FWA) in healthcare services. The report describes the challenges faced by healthcare organizations in managing FWA due to the high volume of claims processed annually. It details the development of a custom dashboard that integrates machine learning results to assist fraud investigation teams. The report explains the technical evaluation of claim data, operational process reviews, and model development, which included training the ML model on both fraudulent and clean historical claims. The report further discusses the ongoing retraining and validation of the model based on new data and feedback from the fraud team. Results indicate significant financial benefits, including estimated savings of nearly $1 million for the organization. The report emphasizes the transition from reactive to proactive fraud prevention strategies through the use of predictive analytics.