Gallagher Re Teams Up With Scrub AI

The latest partnership news for you;

Scrub AI, the AI-powered insurance data ingestion platform, has announced a new partnership with Gallagher Re, one of the world’s leading reinsurance brokers and part of Gallagher, to automate the processing of property binder bordereaux and exposure data for catastrophe modelling.

Working with coverholders and programmes around the world, Gallagher Re receives large volumes of this data in different formats and levels of detail. Preparing it for modelling has traditionally required significant manual effort, either managed internally or outsourced offshore.

Using Scrub AI will help Gallagher Re reduce that manual effort and improve turnaround times on data prepared for catastrophe modelling.

With Scrub AI, incoming bordereaux and schedules of values (SOVs) are automatically cleaned, validated and standardised into catastrophe-model-ready formats. The platform applies Gallagher Re’s own data standards and modelling assumptions, ensuring consistent, traceable outputs regardless of the original data source.

Built on deterministic machine learning models trained on millions of rows of real-world insurance data, Scrub AI enables Gallagher Re to automate a previously time-consuming process while maintaining control over how its data is mapped, coded and validated.

Alex Ley, Co-founder and Chief Executive Officer at Scrub AI, commented:

“Insurance has always been a data business, and today the speed and accuracy of that data increasingly decides how well risk is understood and placed. Gallagher Re is showing what that looks like in practice, giving its teams faster access to data they can rely on, so more of their expertise goes into the modelling and advice their clients value. We’re proud to be supporting them as they lead the way.”

The partnership reflects Scrub AI’s continued growth across the insurance and reinsurance market, with a growing number of insurers, MGAs and brokers using its technology to improve data quality, reduce operational overhead and process increasing volumes of data without adding resources.