Product design and pricing in the insurance industry require extraordinarily technical data analytics to calculate a multitude of variables – risk, demand, history and patterns of claims, as well as projections about future trends and the incidence of unforeseen events, like floods.
This complex task relies heavily on multiple data inputs to conduct sophisticated analytics to deliver the right actuarial outcomes. This data ranges from the most basic demographics – for instance, age and gender – to highly specific information about individual consumers and their behaviour, traffic statistics, weather patterns and other peril data.
While some data is open source – freely available without constraint – other data is more difficult to acquire and use. A typical example is personally identifiable information (PII), which is tightly governed by legal protections on data privacy. All this can pose a challenge for an industry that depends on granular information to determine the appropriate level of pricing and the most competitive products.