The New Architecture of Payer Intelligence
DOI:
https://doi.org/10.5281/zenodo.22873238Keywords:
AI-Enabled Payer Intelligence, Healthcare Claims Processing, Automated Claims Management, Claims Adjudication Systems, Fraud Waste and Abuse Detection, Healthcare Data Pipelines, Enterprise Data Governance, Predictive Claims Analytics, NLP in Healthcare, Natural Language Understanding, Claims Lifecycle Automation, Human-in-the-Loop AI, Healthcare Cost Optimization, Intelligent Claims Routing, Data Integration in Healthcare, AI in Health Insurance, Operational Efficiency in Payers, Healthcare Analytics Systems, Decision Support in Claims, AI-Driven Healthcare Operations.Abstract
This paper presents an overview of AI-Enabled Payer Intelligence in healthcare, focusing on the automation of claims processing—a core function of every health insurance company in the United States. Schmitt and Gamasai examine how an integrated claims and data pipeline, supported by thoughtful data-sourcing strategies, strong enterprise data governance, and end-to-end process automation, can improve operational efficiency while reducing costs. Central to this approach are automated fraud, waste, and abuse detection; natural language understanding for claims adjudication; and predictive modeling applied across the upstream, midstream, and downstream stages of the claims lifecycle to complement human decision-making.
Automated claims processing is a foundational component of payer operations across North America. By combining intelligent claims-routing systems, adjudication-focused natural language understanding models, and generative and predictive modeling techniques within a human-in-the-loop framework, payers can achieve greater efficiency, reduced costs, improved quality, and enhanced member and provider experiences throughout the claims lifecycle.
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