The next big breakthrough within the field of data and analytics is already here and according to Krista Griggs, Head of Financial Services and Insurance at Fujitsu UK, is going to fundamentally alter the way insurance is underwritten.
In a recent interview with Reinsurance News, Griggs described the arrival of digital twin technology as the big data and analytics breakthrough that has already occurred.
“For the first time, for large complex machines like the jet engines or the wind turbines, we are going to be able to have a digital twin of that machine.
“So, you’re able to, by using data, replicate and understand when preventative maintenance is required on a date diagnosis before even an incident arises, so that you can go about and fix it,” said Griggs.
In general, she explained, this will be good for society because ultimately you’ll have less breakdowns, less liabilities, and less large incidents, all of which is going to be better for communities.
“I think that’s the first big breakthrough. So digital twin technology is going to be existing in pretty much all our lives, whether that is machinery or a digital twin of ourselves, where effectively our data is going to help us to understand ourselves better, and where our challenges are existing even better. So, that is going to be fundamental,” said Griggs.
The next step, according to Griggs, is having the ability to process the data and to be able to get meaningful insights from that data, via the use of advanced technologies such as quantum computing.
“Being able to then have faster computation of previously unsolvable problems, is going to help us make that breakthrough of being able to not just consume that data, but also make it actionable in almost near real time, which previously wouldn’t have been possible,” she explained.
Concluding that: “Those two are going to fundamentally change, in my opinion, the way insurance is currently underwritten. And, by the year 2030, in my opinion, we are going to move from what has been previously a repair and replaced model and continues to be, to what is going to be a predict and prevent model.”