Detecting Healthcare Insurance Fraud Using Markov Observation Model
Gyimah, Michael Danso · ERA: Education and Research Archive (University of Alberta) · 2025
Healthcare insurance fraud poses a significant threat to both the financial sustainability of insurance funds and the delivery of quality healthcare services. Rising costs, evolving fraudulent schemes, and the limitations of traditional rule-based detection systems necessitate more sophisticated analytical methods. This thesis introduces the Markov Observation Model (MOM), an extension of the Hidden Markov Model framework, specifically designed for sequential data where each observation depends on both its immediate predecessor and a hidden state sequence. By leveraging counting processes and incorporating three canonical fraud types—Switch Service Fraud, Overcharge Fraud, and Forged Fraud—the MOM effectively captures nuanced behaviors commonly overlooked by conventional techniques. To evaluate the performance of the proposed model, a simulated healthcare insurance dataset was created to mirror real-world complexities. The research details how MOM’s transition probabilities and emission structures are estimated via an iterative Expectation- Maximization algorithm and demonstrates how these estimates translate into practical fraud detection. Comparative analysis against a Random Forest classifier illustratesMOM’s improved accuracy in identifying illicit claim patterns, particularly in scenarios where conventional supervised learning methods struggle to detect subtle anomalies. The results highlight MOM’s potential to serve as a robust and scalable tool for healthcare fraud detection, offering enhanced interpretability of providers’ “mindsets” and adaptability to a broad range of fraudulent schemes. Concluding with a discussion on the model’s limitations and avenues for future research, this thesis underscores the importance of combining advanced statistical techniques with domain expertise to better safeguard healthcare systems.