Health insurance fraud is a large and growing problem throughout Europe, with one report putting the cost of fraud at roughly 56 billion euros. This cost burden is frequently born out by both the healthcare system and consumers, but there are methods technologists and insurers can use to detect fraud. In recent years, our colleagues have implemented anomaly detection techniques in different fields, such as health insurance and retail, and in several areas such as fraud detection, performance optimization and data quality improvement. In this paper, we will explore these areas by looking at some use cases and provide more insight into the different anomaly detection techniques.
Anomaly detection techniques in fraud detection, performance optimization, and data quality
Methods to detect anomalies can be used to find fraudulent claims in insurance, especially in products with a large frequency of payments, such as in healthcare.