Autonomous Fraud Defense for Cloud-Native Insurers
Keywords:
Insurance Fraud Detection, Fraud Detection-as-a-Service (FDaaS), Cloud-Native Fraud Architecture, Agentic AI Systems, Agent-Based Fraud Detection, Causal Fraud Modeling, False Positive Risk Management, Data-Driven Decision Systems, Real-Time Fraud Analytics, Microservices-Based Architecture, Serverless Control Plane, Data Pipeline Architectures, Cloud Data Ecosystems, Data Governance Frameworks, AI-Driven Risk Detection, Privacy-Preserving Fraud Analytics, Regulatory Compliance in Insurance, Scalable Fraud Detection Systems, Intelligent Risk Advisory Systems, Autonomous Fraud Detection Agents.Abstract
Fraud remains a critical challenge for the insurance sector, which increasingly relies on Data-Driven Decision systems for its identification and prevention. Cloud and Data Ecosystems now enable the use of expanded sensor networks, supporting smarter and more responsive insurance products. An evolving Cloud-Native Architecture built on Fraud Detection-as-a-Service allows fraud detection Agentic services to be rapidly deployed and decommissioned as needed. However, two critical dimensions—the scaling of Agentic properties and the incorporation of settlement Risk from False Positives—are often neglected or addressed too late in Cloud-Native Fraud Detection-as-a-Service implementations, even though Agentic properties are essential to scaling these services effectively in the cloud.
To address this gap, Causal Advisory-Multi-Method Evaluative Agentic Fraud Detection offers a framework for incorporating Agentic properties into Cloud-Native Fraud Detection-as-a-Service, applying a risk-causal perspective to their evaluation and modeling. Complementing this, a Cloud-Native Framework for Agentic Data Governance is proposed to ensure Data Quality, Privacy, and Regulatory Compliance across Fraud Detection-as-a-Service operations, while also enabling ongoing monitoring of settlement Risk from False Positives. The resulting architecture follows a Data-Pipeline-driven deployment pattern, built on a Microservices base with a Serverless Control Plane.
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No new datasets were generated or analyzed in this study. The analysis is based on secondary data and published literature cited in the references.
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