As insurers adopt cloud-first strategies to modernize legacy systems, artificial intelligence (AI) is emerging as the engine for large-scale operational transformation. This session explores how global insurance companies are leveraging cloud-native AI to streamline underwriting, accelerate claims processing, and reduce administrative overhead—without sacrificing reliability, compliance, or customer trust.

Through real-world case studies, we’ll examine how enterprises are deploying modular, cloud-based architectures that integrate machine learning, natural language processing (NLP), robotic process automation (RPA), and predictive analytics. These systems are not just augmenting human decision-making—they're autonomously processing thousands of transactions per hour with near-perfect accuracy, auto-triaging over 85% of claims on first pass, and enabling up to 70% automation in daily operations.

The talk will showcase a scalable blueprint built on cloud computing principles, covering microservices design, container orchestration, serverless pipelines, and continuous integration of ML models. We’ll also explore real-time telemetry, monitoring, and analytics strategies for cloud-hosted AI workloads, all aligned with security and governance standards essential to the insurance industry.

Attendees will gain actionable insights into how intelligent automation on the cloud has driven measurable outcomes: cutting claims cycle times by more than half, reducing underwriting decisioning from weeks to hours, and improving fraud detection rates by over 60%.

Whether you’re a cloud architect, data engineer, or enterprise technologist, this session will equip you with patterns and practices to implement resilient, intelligent, and highly available AI systems in cloud-native environments. The talk draws from production implementations at scale and is backed by both quantitative metrics and strategic insights.