Claims automation is no longer experimental for life insurers — it is becoming a core operational capability that reduces cycle time, lowers cost, improves accuracy, and reshapes the customer experience. Early wins came from rule-based automation from RPA, OCR, and digital forms. Advances in machine learning, natural language understanding, and generative models — and now agentic AI (i.e., autonomous, goal-directed AI agents) — promise changes that go beyond task automation to automated decision orchestration, complex case management, and continuous learning across the claims lifecycle.
The life insurance industry is at a pivotal moment where claims automation is transitioning from experimental to essential. Modernizing claims processes is urgent due to several factors: operational inefficiencies, rising customer expectations for speed and transparency, and regulatory pressures. The evolution of claims automation has been slow with early digitization efforts starting decades ago, but now the integration of advanced technologies like machine learning, natural language understanding, and agentic AI are showing the benefits of automation.
After reviewing the market context, the current claims process, pain points in the process, and the evolution of claims automation, the rest of the paper looks at how advanced automation — specifically agentic AI — can rearchitect the life claims lifecycle to accelerate “straight-through” processing where appropriate, reduce leakage from fraud or error, and free human professionals to focus on high-sensitivity cases and customer support.
