The insurance industry is no stranger to predictive analytics.
In fact, the insurance industry lives or dies on its ability to analyse large volumes of data and calculate risk – the better an insurer can understand the future risks and combine different permutations, the better they can serve themselves and their customers.
After all, where would the motor insurance industry be now if it had offered every single driver the same premium, regardless of age, lifestyle or vehicle?
Nevertheless, the true potential of predictive analytics in the insurance industry is yet to be realised.
The nature of analytics technology to date has meant that the process has been time-consuming and oftentimes divorced from the business or customer context. In addition, the industry itself is increasingly fluid, with growing complexity from connected devices, the IoT and ‘smart driving’ trends.
Ultimately, the approach to deploying predictive analytics needs to change.
And the key will be ensuring that it is as accessible and responsive as the industry now demands.
Thankfully, new technology is making this easier – with more modern, self-service platforms now designed for ‘Citizen Data Scientists’ who can run more rapid analysis as and when needed.
Predictive analytics done at speed in this way, with machine learning also layered in, will allow insurers to make multiple predictions in parallel and ultimately provide a completely personalised premium for every single customer.
Indeed, a number of online companies such as Go Compare or Confused.com are already taking advantage of these capabilities, such that organisations that aren’t able to navigate this landscape more nimbly will soon get left behind.
We’ve only just begun to mine the potential of predictive analytics.
But the more we can harness data to create deeper understanding, the more we can manage risk more efficiently, identify new markets and deliver personalised offers to customers.
Outcomes that the future insurance industry will live or die by.