Exposure data sits at the heart of effective catastrophe and hazard modelling. Yet anyone who has worked with large Schedule of Values (SOV) datasets knows that preparing this information for modelling can be one of the most time-consuming steps in the workflow. Inconsistent formats, incomplete fields, duplicated entries, and poorly structured address data often create significant delays before meaningful analysis can even begin.

To address this challenge, we’re excited to introduce AI Data Scrubbing, our newest tool designed to automate the cleaning and geolocation of exposure data at scale. The goal is simple: help teams move from raw SOV files to modelling-ready datasets faster and with far less manual intervention.  

‍The Data Challenge in Exposure Modelling

Exposure datasets frequently arrive in Excel spreadsheets compiled from multiple sources—brokers, policy systems, coverholders, or internal underwriting tools. While these datasets contain critical information about insured assets, they often lack the standardisation required for hazard modelling platforms.

Common issues include:

• Inconsistent column naming and formatting
• Missing or incomplete location details
• Duplicate rows or conflicting entries
• Address fields that require significant manual correction before geocoding

These problems can quickly compound when working with large portfolios, turning what should be a straightforward step into a significant operational bottleneck.

Automating the Data Preparation Process

AI Data Scrubbing is designed to remove much of this friction by using artificial intelligence to automatically clean, standardise, and geolocate SOV datasets. Once an exposure file is uploaded, the tool processes the data and transforms it into a structured, modelling-ready format.

Instead of spending hours manually reviewing spreadsheets, exposure teams can focus on analysis and risk insight while the system handles the underlying data preparation.

Key Capabilities

The first release of AI Data Scrubbing includes several core capabilities designed specifically for exposure modelling workflows:

• AI-driven SOV data cleaning and formatting
  Automatically identifies and standardises inconsistent data fields, helping convert raw spreadsheets into structured datasets.
• High-quality geolocation from address-level inputs
  Extracts and interprets address data to generate accurate geographic coordinates suitable for hazard modelling.
• Bulk processing at scale
  Supports batch processing of up to 10,000 locations per file, with expansion to 100,000 locations coming soon.
• Modelling-ready outputs
  Cleaned and geolocated datasets are delivered in formats that can be used directly across our hazard and peril models.

Built for Exposure and Risk Teams

AI Data Scrubbing has been developed with the needs of insurers, brokers, coverholders, and exposure management teams in mind. By automating the early stages of the modelling pipeline, the tool helps remove operational bottlenecks and enables faster movement from raw exposure data to actionable risk insights.

For organisations managing large portfolios or frequently receiving new SOV submissions, this can significantly improve efficiency and consistency across modelling workflows.

A Collaborative Beta Phase

AI Data Scrubbing is currently being released in beta, and we are actively working with early users to refine the platform. Feedback during this phase will help shape future functionality, improve data handling accuracy, and expand capabilities as the tool evolves.

Our goal is to build a system that not only automates data preparation but also becomes an integral part of the exposure modelling workflow for risk professionals.

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