Agentic AI for Next-Generation Insurance Platforms: Autonomous Decision-Making in Claims and Policy Servicing
Autonomy in AI can improve trust, scalability, efficiency, and responsiveness. This is particularly pertinent in claims processing and policy servicing, where labour needs are diminishing but demand peaks are increasing. An insurance technology platform that uses agentic AI to automate these activ- ities would enhance the speed, quality, and efficiency of service delivery. If these autonomous systems are made trustworthy, their deployment would not only satisfy current labour shortages but also improve user sentiment in engagements that have traditionally been painful, frustrating, and expensive to manage. Acting on behalf of insurance companies, agentic AI would decide whether a claim should be settled or lead to escalation for further assessment. In the course of a policy life, agentic AI would address servicing requirements such as endorsements, renewals, and compliance checks, adjusting pricing in real time as new information becomes available. These transformations would benefit insurers whose investment in agentic AI remains aligned with the appropriate architectural paradigms—modular architecture focused on business goals, privacy-by-design data architecture, and decision-making frameworks that establish certifiability boundaries in low-risk domains such as claims and servicing. The links between claims processing, policy servicing, and architectural paradigms are further explored in the corre- sponding sections. Index Terms—Agentic Artificial Intelligence, Autonomy, Trust- worthy AI, Insurance Technology, Claims Processing, Policy Ser- vicing, Scalability, Efficiency, Responsiveness, Labour Shortages, Service Automation, Decision-Making Frameworks, Privacy-by- Design, Modular Architecture, Real-Time Pricing, Compliance Checks, User Experience, Certifiability Boundaries, Low-Risk Domains, Digital Transformation.