Healthcare Fraud Detection

An analytics platform that identifies fraudulent patterns in healthcare claims data using statistical analysis and machine learning techniques.

Healthcare fraud costs billions annually. Traditional detection methods rely on manual audits and simple rule-based systems that miss sophisticated fraud schemes and generate high false positive rates.

Built data pipelines that analyze claim patterns, provider behavior, and patient histories to identify anomalies indicative of fraud or abuse. The system uses unsupervised learning to detect unusual patterns and supervised models to classify high-risk claims for investigation.