Re/insurers can now accurately model liability exposure probabilistically across their entire portfolios, Lloyd’s has claimed, thanks to a new data-driven methodology the market has developed in partnership with modelling company Arium.
The new approach categorises casualty events based on a company’s business activities –its products and services, operations and infrastructure – and maps the economic relationships that reflect the journey of products and services through the economy.
The methodology creates liability “storm tracks”, which provide a new, structured way of analysing casualty events, regardless of risk classification. This allows insurers to model liability risk in more detail than they have previously, in a way similar to how they model catastrophe exposure.
The methodology is the culmination of a three-year project between Lloyd’s and Arium to improve insurers’ understanding of liability risk exposure. Some brokers have praised the result of the project calling it a ‘valuable milestone’.
Jon Hancock, Lloyd’s performance management director, said: “This is a tremendously exciting development. It is in everybody’s interest to classify liability risks as accurately as possible, and this methodology represents a real step forward for the industry. Of course, for it to work effectively it is dependent on high-quality industry classification data, and I would encourage all brokers and other stakeholders to help with the collection of such data.”
Trevor Maynard, Lloyd’s head of innovation, said: “I am delighted that Lloyd’s has helped to incubate Arium from concept stage to a platform supporting portfolio management as well as deterministic and stochastic modelling of liability exposures. It is very positive that AIR Worldwide has purchased Arium to further commercialise this tool.”
Robin Wilkinson, CEO of Arium and managing director of AIR Casualty Analytics, added: “Arium is pleased to have collaborated with Lloyd’s and the insurance market on the development of this liability tool, and now a methodology for a stochastic model. This framework can facilitate the further understanding of liability accumulations, through combining expert knowledge with data-driven analytics.”
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