Integrating Machine Learning Models with Business Rule Triggers to Boost Performance in Health Insurance Fraud Detection: A Case Study

Health insurance fraud is a significant problem for the insurance industry, where it causes billions of dollars in annual losses. This article describes a novel approach to fraud detection in health insurance that integrates machine learning models with business rule triggers to identify unusual patterns in claims data and flag them for further investigation. Combining machine learning models with business rule triggers greatly enhanced performance across all models. Notably, the approach substantially improved the ability of a model to identify fraudulent cases, leading to a significant increase in effectiveness. This improvement promises to help the insurance industry mitigate the financial impact of fraud.