By Manan Sagar, CTO for Insurance at Fujitsu
Deep down at the bottom of the ocean are devices that measure pressure. So if a tectonic plate shifts beneath the seabed, and water levels begin to rise (even by just a centimetre), the alarm is raised that a tsunami may be on its way to a shoreline. Of course, it’s not something that can be stopped, but it is something that can be managed to limit its destruction. People get to safety, and some valuable possessions can be removed from the area.
Now a burst pipe or a cancelled holiday may not cause the same mass destruction as a gigantic wave sweeping through a city. However, the predict and prevent approach used for natural disasters can equally be applied to disruptions by insurance companies. Not only does this potentially decrease the level of fallout customers have to deal with, it’s also likely to save money for both the insurer and customer.
For example, if we used tech such as artificial intelligence (AI), deep machine learning and cognitive neural networks to track factory machines, data can tell us when those machines will need repairs or replacing. It stops machines from gradually deteriorating until there’s a massive break down by triggering preventative actions at the moment just before there’s an issue. This results in the customer spending less money on repairs and losing money on downtime. It also saves insurance companies money as they avoid funding larger claims, and the money they save can contribute back to business.
At the heart is data
Data is core to a “predict and prevent” model. It holds great importance and is becoming more and more integral in an industry which has always used data, just not in real time. And with an explosion of digital technology, real-time data is becoming increasingly available to analyse water pressure, personal fitness, how we drive, the status of machine components and much more.
By collecting data from incidents as they happen, insurers can understand the common signifiers of potential risks that lead to claims. With this insight, insurers are more accurately able to predict high-risks and be prepared to deal with them faster.
It’s this speed and convenience that insurers can provide by using the predict and prevent approach, which builds trust with customers. In turn, an insurer can continue to guarantee their profitability and competitiveness.
How to tackle a wave of change?
Strategy will have to change for insurers intending on gaining the benefits of a predict and prevent approach. Yet this can be an overwhelming starting point, especially when there’s so many technologies that could potentially accelerate your services. So, first off, start simply, by asking yourself how and why you want your business to adopt a predict and prevent approach.
Then, you need to look at the practical side of things. How can you untangle your current infrastructure and adapt it to meet the aims of your new strategy? This can be equally challenging as numerous legacy systems can make automation and data management complex (which will be essential for predicting and preventing). So, you’ll have to audit your IT landscape and work out decisions for the future of your tech and decide on its relevancy.
Some insurers decide to go about this task on their own, but others choose to work with external consultants. What’s important is that you ensure what’s being put in place will not become redundant in a few years, and that your company culture can be managed positively through the transition.
The goal with your IT is to essentially ensure you’re able to capture as much data as is necessary to gain a full a view of your customer as possible. With this, you’ll have more accurate data which means you’ll be able to make more precise predictions. With time, trust will grow between you and your customers because your actions prove that you’re right.
Tech and data now have the primary role in insurance
Of course, data can only be harvested at the scale and speed the insurance industry requires through technology. So, what tech should you implement?
A cloud infrastructure capable to support gathering and processing of data at pace is going to be a pre-requisite. By using cloud technologies to collect large amounts of data, you can begin to build predictive underwriting models where risks are captured in real time using live data feeds.
Deep insights obtained from data is going to be vital, so this means attention should be focused on technologies such as robotic process automation, advanced analytics and deep machine learning.
Although insurers have been slower in their digital transformation in comparison to other financial sectors, the challenging recovery process following Covid-19 disruption is only going to increase the need for these technologies and the speed they can offer. For example, building resilience is going to be key, and this is only going to be possible if insurers have the insights to plan for extreme scenario planning.
A time would come where there would be no choice but for insurers to digitally transform – that time is now.