Adaptive Data Meshes for Claims Efficiency and Fraud Resilience
DOI:
https://doi.org/10.5281/3pd2js41Keywords:
Cloud-Scale Data Fabrics,Cognitive Data Management,Intelligent Claims Processing,Fraud Detection and Prevention,AI-Driven Insurance Analytics,Real-Time Claims Optimization,Machine Learning for Fraud Resilience,Enterprise Data Integration,Predictive Risk Intelligence,Adaptive Decision Support Systems.Abstract
Cloud-based cognitive data fabrics—built on platforms such as Google Cloud, AWS, or Azure—enable intelligent, predictive, real-time big-data solutions for insurance claims processing and query handling. This work addresses two interdependent challenges: (1) optimizing the claims process through intelligent extraction, normalization, and automated response generation, and (2) building resilience against fraudulent behavior through adaptive analytics.
Cloud-scale cognitive capabilities support two core functionalities: claim-process optimization and fraud resilience. The underlying computation model operates across layers of reasoning and knowledge, supporting either localized (topic-specific) reasoning engines or a single, more advanced engine for global, cross-domain reasoning over complex events.
Three claim-optimization strategies are proposed: (1) extracting claims from unstructured data sources and automatically normalizing them into structured form; (2) using historical claim patterns to automatically respond to incoming service or insurance requests; and (3) detecting anomalous patterns in insurance queries and requests to identify, score, and reduce fraud risk.
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No new datasets were generated or analyzed in this study. Data sharing is not applicable to this article.
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