The latest recruitment news from Angelica Solutions;
Angelica Solutions welcomes two trusted industry professionals to its portfolio of insurance collaborators with Matt and Katherine Goldsmith.


Katherine and Matt bring a combined 35+ years of pricing and underwriting expertise across motor, home, travel, and commercial vehicle insurance, with experience spanning global insurers, start-ups, and major players such as AXA, Allianz, eSure, and Swiftcover. Katherine’s background in claims modelling, commercial pricing, data strategy, MI reporting, and technical underwriting matched with her analytical approach and strategic mindset provides a valuable complement to Angelica Solutions’ existing strengths bolstering its ability to deliver commercially impactful, data-led solutions to clients navigating complex risk and pricing challenges.
Matt complements this with deep technical capability, having built and maintained rating engines using Radar and Python, whilst automating testing and deployment via CI/CD pipelines, he’s used his expertise to drive pricing transformation across international teams. As an ISTQB professional proficient in cloud technologies, his technical know-how and commercial awareness further strengthen Angelica’s ability to deliver scalable, future-ready solutions.
“We’ve always respected the way Sarah and her team operate which is driven by a genuine desire to do good work,” said Matt Goldsmith. “Joining the associate network felt like a natural step, and it’s great to work alongside people who know what good looks like and how to deliver it.”
Founded in 2019 by Sarah Vaughan, Angelica Solutions brings deep industry expertise and a commercially focused, practical approach to solving complex challenges for insurers and MGAs. Most recently, the team partnered with Howden Driving Data on the first independent study of its kind, analysing over 1.2 billion miles of telematics data from young driver policies. The findings demonstrated that Howden’s proprietary HDD Driver Score is a significantly stronger predictor of claims than traditional insurance rating factors alone.

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