BirdsEyeView, a European Space Agency-backed Insurtech specialising in natural catastrophe modelling and exposure management, has launched AI Data Scrubbing, a new capability designed to automate Statement of Values (SOV) data cleaning and geolocation to support large-scale hazard modelling.
AI Data Scrubbing uses advanced artificial intelligence (AI) to clean, standardise and geolocate submitted Excel SOV files automatically, transforming raw exposure data into modelling-ready inputs within minutes.
The solution aims to remove the bottleneck in catastrophe modelling of preparing exposure data before it can be analysed. Automating this process enables insurers and brokers to accelerate time-to-insight while improving overall data quality and strengthening modelling confidence.
Key capabilities include 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 in upcoming releases), and outputs optimised for hazard modelling across multiple peril models.
Developed in collaboration with insurers, brokers, coverholders and exposure management teams, AI Data Scrubbing reduces friction at the earliest stage of the modelling pipeline, allowing teams to move from raw data to actionable risk insights 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.”
