BirdsEyeView Launches 'AI Data Scrubbing’ for Large-Scale Hazard Modelling
By Lauren Towner · 5 March 2026
BirdsEyeView, the European Space Agency-backed insurtech specialising in natural catastrophe modelling and exposure management, announces the launch of AI Data Scrubbing, a new capability designed to automate Statement of Values (SOV) data cleaning and geolocation to support bulk hazard modelling at scale.
AI Data Scrubbing applies advanced AI to automatically clean, standardise, and geolocate submitted Excel SOV files, transforming raw exposure data into modelling-ready inputs within minutes.
The solution is designed to remove one of the most persistent bottlenecks in catastrophe modelling: preparing exposure data before it can be analysed. By automating this process, insurers and brokers can accelerate time-to-insight while improving overall data quality and modelling confidence.
Key capabilities:
- AI-driven SOV data cleaning and formatting
- High-accuracy geolocation from address-level inputs
- Bulk processing of up to 10,000 locations per run (scaling to 100,000 locations in upcoming releases)
- Outputs optimised for hazard modelling across multiple peril models
Built in collaboration with insurers, brokers, coverholders, and exposure management teams, AI Data Scrubbing reduces friction at the earliest stage of the modelling pipeline, enabling teams to move from raw data to actionable risk insight significantly faster.
James Rendell, CEO and Founder of BirdsEyeView, said: “Exposure data is the foundation of every catastrophe modelling decision, yet preparing it is still one of the most manual and error-prone parts of the workflow. Teams spend huge amounts of time fixing inconsistent formats, filling data gaps, resolving duplicates, and correcting addresses before they can even begin modelling.
“With AI Data Scrubbing, we are fundamentally changing that experience. We’re giving underwriters and brokers the ability to take large, messy datasets and turn them into high-quality, geolocated, modelling-ready portfolios in minutes at their desk.
“Longer term, this is about more than efficiency. Clean, structured exposure data unlocks better modelling accuracy, faster underwriting decisions, and ultimately better risk selection. As portfolios grow and catastrophe risk becomes more complex, the ability to scale data quality quickly will be a real competitive advantage for the market.”
BirdsEyeView’s platform combines high-resolution satellite data with advanced AI analytics to deliver real-time risk assessment and live portfolio exposure management directly at the underwriter’s desktop. Companies in this story: BirdsEyeView
People in this story: James Rendell