XL Catlin’s internal innovation team, Accelerate, has teamed up with Cytora, a UK-based Insurtech start-up that uses artificial intelligence (AI) and open source data to improve the way insurers quantify, select and price risk.
Cytora’s risk engine scrapes and analyses information from the internet, including data from company websites, news articles and government datasets and processes it using AI algorithms in order to predict future claims, attractive risk profiles and quality of risks.
XL Catlin will use Cytora’s expertise to create new risk insights in a bid to improve underwriting and identify new opportunities. The company said that the insights generated will “help actuaries at portfolio level to identify new profitable segments, help underwriters to improve risk selection and provide tailored risk solutions for clients.”
Commenting on the partnership, CEO of Accelerate Vincent Branch said: “We are experimenting with a wide range of cutting-edge technologies to explore what is possible. But we cannot do it alone. We need to partner with like-minded start-ups like Cytora that can help us embed those new technologies to transform our product design, pricing, underwriting, claims and risk engineering capabilities to the benefit of our clients and brokers.”
“We are excited to be working with Cytora, a company that has, in just three years, built such an excellent reputation around its ability to use artificial intelligence and machine learning to help drive better business decisions,” he added.
Cytora was founded in 2014 by a multidisciplinary team and spun out of the University of Cambridge. According to Crunchbase, the start-up has raised $3.48mn of equity funding since October 2015 from eight investors which include Parkwalk Advisors, Cambridge University Enterprises and former RMS executive Matthew Grant.
XL Catlin established its Accelerate team in November 2016, to leverage commercial opportunities arising from new technologies and to drive transformational innovation by working closely with the business.
