European Space Agency-backed InsurTech BirdsEyeView has launched AI Data Scrubbing, a new capability designed to automate Statement of Values (SOV) data cleaning and geolocation to support bulk hazard modelling at scale.
The capability applies advanced artificial intelligence to automatically clean, standardise and geolocate submitted Excel SOV files, transforming raw exposure datasets into modelling-ready inputs within minutes.
Preparation of exposure data remains one of the most persistent bottlenecks in catastrophe modelling workflows, with teams often required to manually standardise formats, resolve missing information and correct addresses before risk analysis can begin.
Automation through AI Data Scrubbing enables insurers and brokers to accelerate time-to-insight while improving overall data quality and modelling confidence.
Key capabilities include AI-driven SOV data cleaning and formatting, high-accuracy geolocation using address-level inputs, and bulk processing of up to 10,000 locations per run, with future releases expected to scale to 100,000 locations.
Outputs generated by the system are optimised for hazard modelling across multiple peril models.
Development of the capability was carried out in collaboration with insurers, brokers, coverholders and exposure management teams aiming to reduce friction at the earliest stage of the catastrophe modelling pipeline.
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.
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