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Zurich Insurance
Insurance · UK
Flood risks predicted weeks in advance
Zurich Insurance predicts flood claims weeks before they happen, shifting from reactive to proactive risk management
AI system links customer addresses to weather and geo-data. Proactive warnings replace reactive claims processing.
Friction
Traditional insurance reacts to disasters. Zurich wanted to predict whether and when customers would make claims so they could warn them in time.
Breakthrough
A predictive parametric model: customer addresses linked to UK Unique Property Reference Numbers and external weather and geodata, run through SageMaker, to generate flood risk predictions weeks ahead.
Impact
Flood risks signalled weeks in advance. Development cycle reduced from 26 to 8 weeks. Operating model shifts from reactive claims to proactive risk prevention.
Unlock the full analysis with breakthrough, impact, what made it smart and its technical approach below!
Problem
Traditional insurers react to damage. Zurich wanted to predict flood claims before they occur, warning customers in advance and activating mitigation plans.
What made it smart
The combination of property-level data, external geo and weather data, and MLOps infrastructure that can generate proactive warnings weeks before an event. This breaks the reactive cycle that defines traditional insurance.
Technical approach
End-to-end MLOps architecture on AWS. Customer locations anonymised and linked via UPRN to geographic and meteorological data. Snowflake as strategic data hub. Amazon SageMaker for model training and inference. Human-in-the-loop validation before customer alerts are sent.
Strategic lesson
Insurance AI creates the most value when it shifts the operating model, not just automates the existing process, but replaces reaction with prediction.
Reflection question
Which risks in your organisation do you currently manage reactively, because you do not have the data or the model to see them coming?