Agentic artificial intelligence for dynamic claims processing and fraud detection
Balaji Adusupalli · 2025
In the last century, insurance claims processing has gradually moved from being an entirely manual process to being more automated with machine-learning optimization. Machine learning has improved important sub-tasks: Natural Language Processing can convert unstructured documents into structured data, Image Processing can analyze regulators’ photo examinations, and Predictive Modeling can compute estimates based on historical experiences. Consolidation and integration of these models into a full cycle, end-to-end automated claims processing and adjustment system are important areas of research and development. Current automated systems are mostly passive and rudimentary: they merely predict part of the outputs ignore important dependencies, and are limited in their dynamic updating-to-enormous internal databases housing individual unique customer histories and carrier experiences based on geolocation and other important parameters (Ngai et al., 2011; Baesens et al., 2015; Esteva et al., 2019).