Marshmallow Migrant Risk Scoring

The real advantage is not cheaper customer acquisition, it is better risk selection inside a group that legacy insurers often misprice. Marshmallow asks for foreign licence details, overseas no claims history, identity documents, and sometimes telematics, then matches that against a risk database built from more than 1 million migrant drivers. That lets it treat a newcomer with ten safe driving years abroad as lower risk than a blank UK file would suggest, which is how it can cut prices and still improve claims performance.

• Legacy UK motor insurers often price migrants badly because their systems lean on UK claims, credit, and licence history. Marshmallow accepts driving and claim free history from all countries, and its public savings claims show newcomers saving about £220 on average in 2024, with some recent cohorts seeing much larger gaps versus the next cheapest quote.
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• This works because Marshmallow is not just a broker sending leads elsewhere. It owns underwriting, pricing, and much of claims handling, including a repair operation, so when its model identifies a good driver that incumbents reject or overprice, Marshmallow keeps the underwriting margin instead of handing it to a carrier partner.
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• Comparable insurtechs also use non traditional data to unlock ignored segments, but each has a different wedge. Zego leans on telematics for gig and van fleets, INSHUR leans on ride hail platform data, and Cuvva and By Miles price around short term or low mileage behavior. Marshmallow's wedge is migrant identity and driving history data at scale.
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The next step is to reuse this migrant risk graph beyond UK car insurance. As the insured base grows and the model sees more outcomes, Marshmallow can push the same data advantage into van, home, credit, and new geographies where newcomers face the same thin file problem, turning one underwriting insight into a broader financial services moat.