AI-Driven Fraud Detection: Enhancing Claims Analytics with Real-Time Streaming and Behavioral Biometrics

Abstract

Scammers and frauds have always been challenging and at the same time the most undetected threat to the insurer world. We will present the experimental work we have done on our novel pattern discovery framework for fraud detection, which is based on our examination of a historical claims data repository in ClaimCenter, together with real-time data mining techniques to detect emergent fraudulent trends. AI also improves existing rule-based engines by allowing them to incorporate anomaly detection into their existing systems, and to actively respond in order to prevent future fraud attacks. Using graph databases such as Neo4j, you can perform an in-depth analysis of relationships between claims, policyholders, and external actors, uncovering hidden links that could indicate potential fraud. The paper also explores behavioral biometrics and pattern analysis to provide an addition-evolving user behavior profiling as unique, creating an additional security layer that provides a better tool in the toolbox for prevention of fraud. They also explore real-time streaming analytics platforms such as Apache Flink and Apache Spark for continuous monitoring, real-time detection of fraudulent activities, reduced latency, and improved response times. Through this paper, we introduce a holistic framework that embraces these technological developments, demonstrating how they can effectively enhance fraud detection, expedite claim managing, and reduce financial risks. Ultimately, these solutions will democratize a proactive, data-centered approach to addressing fraud, allowing insurers to remain one-third of the way ahead of fraudsters in a particularly complex and fast-moving data world.