Conversational AI and Workflow Automation in Claims Support for Health Plans

The integration of conversational artificial intelligence and intelligent workflow automation in health plan claims processing has transformed administrative operations by improving efficiency, accuracy, and member experience. This paper analyzes architectures, technologies, performance metrics, and implementation outcomes of conversational agents, natural language processing, robotic process automation, and machine learning as applied to claims support functions up to 2020. Evidence from industry reports and case studies shows that automation can reduce end-to-end processing time by 70–90 percent, decrease denial rates from approximately 11 percent to below 4 percent for automated cohorts, and lower administrative cost per claim from about 25 to 12 United States dollars at volumes of 100,000 claims per year. Benchmarked machine learning approaches for adjudication and fraud detection reach accuracy levels of 94–96 percent, significantly surpassing traditional rule-based systems that typically remain below 85 percent. Adoption indicators show that, by 2020, more than half of large insurers had invested in artificial intelligence and that roughly one-third had deployed chatbots or robotic process automation in claims or customer service workflows. The findings demonstrate that integrated architectures combining conversational interfaces, workflow automation, and predictive analytics deliver measurable operational, financial, and service improvements while introducing new challenges in integration, governance, and workforce transformation.