Catastrophic Risk Solution

Xebia’s Agentic AI autonomously evaluates catastrophic events by simulating multiple damage scenarios, gathering evidence, and proposing explainable risk assessments. Unlike static rules engines, these AI agents continuously learn from new climate signals, imagery, policy data, and claims outcomes. This results in faster triage, more accurate severity predictions, and transparent decisions that regulators can audit.

The solution combines generative AI, geospatial analysis, computer vision, and knowledge AI to process satellite imagery, climate feeds, historical CAT loss patterns, policy documents, adjuster notes, and exposure data. By fusing these multimodal inputs, insurers get near real-time visibility into flood, wildfire, storm, and wind-related damage, enabling more accurate underwriting and faster claims settlement.

Xebia uses governed autonomous agents and a Claims Evidence Graph that links every AI-generated decision to source data, regulatory rules, and documented reasoning. This architecture allows insurers to trace conclusions back to evidence, strengthening compliance, reproducibility, and customer trust. It also reduces leakage by ensuring consistent, audit-ready outcomes.

The solution is engineered for seamless integration with AWS, Microsoft Azure, Google Cloud, and Databricks. These partnerships enable high-velocity multimodal data processing, secure model governance, scalable geospatial workloads, and continuous learning pipelines. Xebia also connects to existing policy admin, claims, and risk systems to minimize disruption and accelerate deployment.

Carriers typically achieve faster cycle times, more accurate loss estimates, reduced adjuster workload, and improved customer satisfaction. The system reduces leakage, increases transparency, enhances regulatory defensibility, and speeds up settlement during large-scale events. By combining Agentic AI with deep insurance expertise, Xebia helps insurers deliver fair, explainable results at enterprise scale.