Deep Agents for Cloud-Native Fraud Detection in Insurance
Keywords:
Agentic AI; Cloud-Native Environments; Concept Drift; Fraud Detection; Insurance; Risk Prediction; Time-Series Data; Deep Agentic AI Systems; Real-Time Insurance Fraud Detection; Autonomous Decision-Making Agents; Risk Prediction and Scoring Models; Cloud-Native AI Architectures; Event-Driven Fraud Analytics; Multi-Agent Reinforcement Learning; Streaming Data Intelligence; Explainable AI (XAI) for Insurance; Scalable Mops for Financial Services.Abstract
Insurance fraud contributes greatly to policyholder losses and the yearly drain on industry resources. Existing deterrent methods typically react to fraud events, affecting genuine customers. An approach using Deep Agentic AI to sense abnormal activity and potential fraud is applied. The underlying principle is viewing fraud detection as an agentic activity, employing real-time inference with streaming data analytics to monitor for fraud risk. The proposed architecture integrates microservices with serverless components in a cloud-native design aligned with the features of true agentic AI. Infrastructure is established for autonomous decision-making and incident response, operationalizing real-time fraud detection at scale. Although investigated in fraud detection, the approach is generalizable and applicable across domains in which agentic decision-making is necessary and proven agentic systems are in place.
Detecting fraud in mutual insurance companies, where expenses are borne by policyholders, is globally important. Fraudulent pricing, accident claims, and pay-outs directly reduce profits. Sensitive agents have failed to establish deterrence, as is evident from the rising share of fraud cases. Detecting fraud pre-emptively offers the potential to limit client inconvenience and to avoid incorrect insurance decisions. Investors have indicated a desire for insurance companies to increase investment in AI, with almost three-quarters considering insurance fraud a suitable application. Therefore, the development of appropriate tooling will facilitate meeting investor expectations.
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