Product design and pricing in the insurance industry require extraordinarily technical data analytics to calculate a multitude of variables, such as risk, demand, projections about unforeseen events, like floods, and the incidence of claims.
It’s a very complex task that relies heavily on multiple data inputs to determine the appropriate level of cover. These can range 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 ‘perils’ – also known as ‘acts of God’, among many others.
Some data is open source – freely available without constraint.
Other data is more difficult to acquire and use – such as personally identifiable information (PII) – due to legal limitations related to data privacy. Given the nature of the information needed by insurers, sharing that data to perform complex analytics may fall foul of regulators and customers alike.
In a fiercely competitive industry, it’s essential to deliver finely tuned and highly personalised products at the right price, particularly given the customer perception of insurance as a grudge purchase. And this means the data inputs need to be varied, granular, accurate and not stripped of the very attributes that make it valuable in order to meet privacy and security requirements. As the industry moves more towards the personal lines insurance model, advanced analytics on this type of rich data is becoming a business requirement.