Temporal Feature Engineering and Threshold Optimization for Early Warning in Healthcare Claims Anomaly Detection

Abstract

Healthcare insurance fraud represents a substantial financial burden on medical systems worldwide, with fraudulent claims accounting for billions of dollars in annual losses. Detecting anomalous patterns in medical claims data requires sophisticated analytical approaches that can identify subtle temporal irregularities before significant financial damage occurs. This research presents a comprehensive investigation of temporal feature engineering methodologies and threshold optimization strategies specifically designed for early-warning mechanisms in healthcare claims anomaly detection. The study develops a systematic framework for extracting meaningful temporal features from claims sequential data, including service interval patterns, claim frequency characteristics, and seasonal variation indicators. Advanced feature construction techniques that combine statistical analysis and machine learning are employed to capture the complex temporal dependencies inherent in fraudulent behavior patterns. We investigate threshold optimization strategies that balance detection sensitivity with operational constraints through adaptive adjustment mechanisms. A retrospective case study of Medicare claims data suggests that engineered temporal features can improve anomaly-detection performance. The research provides practical guidelines for threshold parameter selection and dynamic adjustment strategies suitable for production deployment. Results suggest improvements in early warning capability while maintaining practically manageable false positive rates.