BirdsEyeView, a European Space Agency-backed insurtech company specializing in natural disaster modeling and risk management, has launched AI Data Cleansing, a new feature designed to automate statement of value (SOV) data cleaning and geolocation to support large-scale disaster modeling.
AI Data Cleaning uses advanced artificial intelligence (AI) to automatically clean, standardize, and geolocate submitted Excel SOV files, transforming raw exposure data into model-ready inputs in minutes.
The solution is designed to eliminate the bottleneck in disaster modeling of preparing exposed data before analyzing it. Automating this process enables insurers and brokers to accelerate insights while improving overall data quality and increasing modeling confidence.
Key features include AI-driven SOV data cleaning and formatting, high-precision geolocation of address-level inputs, batch processing of up to 10,000 locations per run (scalable to 100,000 in an upcoming release), and output optimized for hazard modeling of multiple hazard models.
Developed in partnership with insurers, brokers, underwriters and risk management teams, AI data cleansing reduces friction at the earliest stages of the modeling process, allowing teams to move from raw data to actionable risk insights faster.
“Exposed data is the foundation of every disaster modeling decision, yet preparing data remains one of the most manual and error-prone parts of the workflow,” said James Rendell, CEO and founder of BirdsEyeView. “Teams spend significant time fixing inconsistent formatting, filling data gaps, resolving duplicates, and correcting addresses before starting modeling.”
“With AI data cleansing, we are fundamentally changing this experience. We enable underwriters and brokers to take large, messy data sets and transform them into high-quality, geo-targeted, modelable portfolios in minutes on their desk.
“In the long term, it’s not just about efficiency. Clean, structured risk data can lead to better modeling accuracy, faster underwriting decisions, and ultimately better risk selection. As portfolios grow and catastrophe risks become more complex, the ability to quickly scale data quality will become a real competitive advantage in the market.”
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