Swiss Re leveraging machine learning to predict motor frequency developments - Reinsurance News

By utilising machine learning and numerical text processing techniques, Swiss Re has been able to generate a "predictive view" of motor frequency developments in several markets. In a recent conversation with Nikita Kuksin, Hhead of modelling within Casualty R&D, Miriam Hook, vice president Global clients and Surbhi Gupta, assistant vice president, casualty R&D at Swiss Re, it was explained to us how these alternative approaches were able to provide added granularity to existing data. "We intended to develop an alternative to traditional actuarial calculation methods that would give us an "external perspective" on claims frequency within our motor portfolio and allow us to predict motor frequency developments in several motor markets," said Kuksin, who leads the modelling team within the casualty research and development department at the Swiss Re Institute. Gupta, who prior to her current role served at Swiss Re for three years' as a data scientist, explained how these methods were brought into fruition by first checking the status quo of frequency developments against external data, before then explaining motor frequency using external data to generate factors that could be projected into the future. "These are complex objectives, requiring solid data sets and robust analytics," Gupta explained.