Simulation and Detection of Healthcare Fraud in German Inpatient Claims Data

The German Federal Criminal Police Office (BKA) reported damages of 72.6 million euros due to billing fraud in the German healthcare system in 2022, an increase of 25% from the previous year. However, existing literature on automated healthcare fraud detection focuses on US, Taiwanese, or private data, and detection approaches based on individual claims are virtually nonexistent. In this work, we develop machine learning methods that detect fraud in German hospital billing data. The lack of publicly available and labeled datasets limits the development of such methods. Therefore, we simulated inpatient treatments based on publicly available statistics on main and secondary diagnoses, operations and demographic information. We injected different types of fraud that were identified from the literature. This is the first complete simulator for inpatient care data, enabling further research in inpatient care. We trained and compared several Machine Learning models on the simulated dataset. Gradient Boosting and Random Forest achieved the best results with a weighted F1 measure of approximately 80%. An in-depth analysis of the presented methods shows they excel at detecting compensation-related fraud, such as DRG upcoding. An impact analysis on private inpatient claims data of a big German health insurance company revealed that up to 12% of all treatments were identified as potentially fraudulent.