UCL School of Management Lecturer Dr. Deyu Ming and Professor Steve Yoo have secured a £200,000 Knowledge Transfer Partnership (KTP) with Howden Re, the global reinsurance, capital markets and strategic advisory arm of Howden, the global insurance intermediary group, marking the school’s first-ever KTP project. Co-funded by Innovate UK and Howden Re, the collaboration will apply cutting-edge artificial intelligence to automate and innovate data processing in the (re)insurance sector, bridging the gap between academic research and commercial applications of AI.
This KTP represents a major step forward for UCL SoM in delivering on its mission to foster innovation and societal impact through deep engagement with industry. Over the course of 18 months, UCL SoM and Howden Re will co-develop AI-powered tools to transform how insurance data is processed - enhancing speed, accuracy, and transparency across the (re)insurance placement lifecycle.
“This KTP not only advances the frontier of applied AI research but also shows how academic–industry partnerships can co-create impactful solutions to complex and real-world data challenges in the reinsurance space.”
— Dr Deyu Ming, Academic Supervisor
“This partnership with Howden Re is a great example of how we can apply cutting-edge research to real-world challenges. We’re excited to see UCL’s AI expertise make a tangible impact in the (re)insurance industry.”
— Professor Steve Yoo, Academic Lead
“We’re thrilled to be collaborating with UCL on this AI product — a first of its kind in the reinsurance space. As risks become ever more complex and interconnected, the role of re/insurance in making sense of uncertainty — and putting a price on it — has never been more vital. With this investment, Howden Re reinforces our commitment to innovation, increasing efficiency and solving data transparency problems in a scalable way.”
— Dr Melodie Vanderpuye, Howden Re Project Supervisor
As part of this collaboration, a full-time KTP Associate will be embedded at Howden Re’s London office, working on-site to lead technical development and deployment of advanced AI workflows. This is an exceptional opportunity for the Associate to gain hands-on experience at the intersection of academic research and industry innovation. The deadline for the position is 02 July 2025.
