Transparent Agentic AI Framework for Insurance Fraud Intelligence and Dynamic Risk Forecasting in Distributed Cloud Environments
DOI:
https://doi.org/10.64137/31079911/IJMST-V1I2P106Keywords:
Explainable Agentic AI, Transparent Fraud Detection, Adaptive Risk Prediction, Multi-Cloud AI Architectures, Insurance Risk Analytics, Explainable Decision Systems, Edge-to-Cloud Governance, Fault-Tolerant AI Frameworks, Real-Time Risk Monitoring, AI Auditability FrameworksAbstract
Explainable agentic AI facilitates transparent insurance fraud detection and adaptive risk prediction in distributed cloud systems. Explaining answer generation for explainable AI enhances transparency and auditability, addresses user-specific variability in requirements, enables opportunity cost and privacy considerations, and provides evidence for defense in supervisory contexts. A multi-cloud, edge-to-cloud architecture secures data governance, lineage, latency, and fault-tolerant constraints. Insurance fraud detection and risk prediction models reside in a transparent agentic framework. Explainable agentic AI enables explanation generation, essential guidance for auditors, underwriters, and regulators in digitalisation learn-to-win environments. Such explanation generation should apply to any risk assessment problem, widening model deployment considerations. Evaluation investigates the quality of explanations in actual contexts. Rapid digitalisation accelerates the diffusion of new risk exposures and the corresponding technical risk models required to manage them. Within the insurance industry, the detection of insurance fraud and the prediction of risk for dynamic data-hungry lines of business such as cyber insurance provide classic examples of preliminary digitalisation touchpoints. Jointly solving these two risk assessment challenges in a multi-cloud setup facilitates the establishment of transparent computational agents capable of monitoring, detecting, and measuring the impact of novel risks on live data feeds in near real time. Do these two models, jointly deployed in a learn-to-win setting, genuinely satisfy answer explanation requirements? Transparent agentic frameworks should provide evidence that models, in actual use or explainable, consider the latent end-user audience and undertake explainable answer generation.
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