Supercharging data strategy: building smarter insurance in the age of information

The good news is not only has there never been so much data, it has never been so up to date and dynamic.

“Real time is becoming more and more important because it means you can contextualise the customer”

 

Raj Mukherjee, Direct Line Group

“Real time is becoming more and more important because it means you can contextualise the customer who is trying to buy insurance,” said Raj Mukherjee, Chapter Area Lead for Data Analytics at Direct Line Group. “You can understand risk much better, you can price it accordingly and deliver it using the right channel at the right time for that customer.”

Tom Spencer of Aviva agreed that customer segmentation is a blunt tool in the age of hyper-personalisation. “There’s no such thing as the average customer,” he said. “That average can hide a multitude of different interactions so having access to really granular data makes a world of difference between the right and wrong decision.”

“There’s been a real sea change in the sophistication of tooling that’s available and the data literacy of underwriters using that tooling”

 

Will Iles, MS Amlin

Will Iles, Head of Analytics and Data Science at MS Amlin, said data can add real value to insurance companies, whether it’s improving customer experience or improving the efficiency of claims by freeing up claims handlers to spend more time on higher value and intellectual tasks. Underwriting is also being transformed by the deployment of data and analytics. “There’s been a real sea change in the sophistication of tooling that’s available and the data literacy of underwriters using that tooling,” said Iles. 

Our panellists also considered the shift to Open Insurance, which may not be as advanced as Open Banking but is definitely on the radar. “It will open up a deluge of data for insurers to get into that ecosystem play and where they can embed insurance services into non-insurance ecosystems,” said Raj Mukherjee of Direct Line.

Supercharging data

When it comes to supercharging data strategies, there are two lines of attack. The first is around using new data sources, such as telematics and other connected devices, to configure new insurance products – using data from a smart appliance to trigger an alert of a possible fault that can be pre-emptively fixed before raising a claim, creating a new kind of insurance model based on a pro-active subscription risk management service rather than a re-active compensation based policy.

It’s not just telematics and connected devices that can deliver these new sources of data. Will Iles of MS Amlin said there were also many hugely valuable publicly available data sets that insurers should be tapping for insight. “I was surprised when I came into the industry how relatively limited the internal data was that was being used to build and justify pricing and risk decisions,” he said. “There are global source-able data sets that can be a valuable proxy for risk and can give more of a 100 per cent view of risk rather than the 2-3 per cent used historically. That’s really supercharging data.”

Raj Mukherjee of Direct Line agreed and pointed out that some InsurTech providers are becoming data providers, drawing on valuable remote sensing geospatial and satellite imagery. “It gives you a really good view of property risk, for example, using that kind of data at the property level rather the postcode level,” said Mukherjee. “It will really change how we look at and quantify risk.”

When it comes to supercharging data, it’s important to look at the potential to overhaul data-driven processes using modern analytics technologies, such as AI and machine learning. From claims to frauds, these technologies can turbo-charge processes to cut out costs, streamline operations and transform experiences. “It can be used for the top-line and the bottom-line,” said Mukherjee.

Tom Spencer of Aviva agreed, saying supercharged data strategies can configure products, services and journeys that are personal and valued by the customer. “It’s all about increasing the relevance,” he said. “It’s not spam if it’s relevant and the goal is to be 100 per cent relevant.”

“It’s important to build scalable solutions and look as the art of the practical as well as the art of the possible.”

 

Matt Hutchins, CAPCO

The big leap forward is moving these supercharged data and analytics projects from the sandbox to commercial operations. Matt Hutchins of CAPCO said he was now seeing a pivot, with insurers now starting to industrialise their use of AI and machine learning (ML) in order to deliver a return on the investment. “It can be cost prohibitive to deliver AI and ML projects at scale, especially when you’re looking at initiatives on a single outcome basis,” he said. “A cost benefit model is key, working out how to reduce the marginal cost of insight as they build more and more over time to make it more affordable. It’s going to be important to build scalable solutions and look as the art of the practical as well as the art of the possible.”

A data-centric culture

It’s not just tools, however. Our panellists agreed that the people aspects – from leadership to corporate culture – are just as important to supercharging data.

Tom Spencer of Aviva said senior sponsorship was key to success. “It needs to come from the CEO down or it’s doomed to fail,” he said. “It’s about mindset. The risk appetite has to change to allow for sustainable growth and that’s before we even get into the challenges around the technology and the processes. The leader needs to walk the walk and make a difference through engagement, awareness and evangelism.”

“The most important issue to tackling legacy data is getting senior leaders bought into the idea that data is a fundamental asset”

 

Will Iles, MS Amlin

Will Iles of MS Amlin agreed that leadership is key when it comes to securing the corporate drive and investment to turbo-charge data. “The most important issue to tackling legacy data is getting senior leaders bought into the idea that data is a fundamental asset,” he said. “It can be a painful process cleaning up legacy data but at the end of that you have a very valuable, commercial useful data set.  It’s important to be able to articulate that value before you go through that painful process.”

This means organisations need to have a blend of skills, bringing together skilled data scientists and engineers with those who understand business needs and can build a narrative around data, technology and outcomes. The shift to agile working is helping, by putting together outcomes-focused multi-disciplinary teams. “Giving them seed funding to build proof of concept, build it in their lab, prove value and then scale,” said Matt Hutchins of CAPCO, who said more insurers are adopting agile working practices.

Will Iles of MS Amlin sounded a note of caution, worrying that the COVID-19 shift to remote working could see data scientists and analysts side-lined. “It’s going to be important to maintain that sphere of influence and keep them aligned with the business so they understand the challenges and pain-points that underwriters are facing,” he said.

“There is no panacea. You just need to get on, look at the data you have, get your arms round it and get some quick wins”

 

Tom Spencer, Aviva

Culture change is always an enormous challenge, coming on top of the data and technology transformation that will be required to be fit for the information age. But Tom Spencer of Aviva said it was important not to be paralysed by the scale of the task, nor to wait for a silver bullet to come along and fix the problem. “There is no panacea,” he said. “You just need to get on, look at the data you have, get your arms round it and get some quick wins.”

Indeed, it seems the worst thing to do would be to do nothing.