Data has evolved as a critical asset

How is insurtech transforming the customer experience?

Martyn Mathews, Lexis Nexis Risk

Solutions: What we’re seeing is data and technology used in new and different ways to provide a more individualised, streamlined, hassle-free experience. Insurtechs are not using data just to validate customers at the point of application, they are using data throughout the continuum, from application through to claim, reducing painful touchpoints, making the complex world of insurance simpler for customers.

At quote, UK insurtechs are leveraging data to reduce or remove question sets altogether to ease the customer experience, improve the underwriting process and offer that personalised policy that busy customers want.

In claims, insurtech propositions tend to offer a touchless experience, often through the use of a smartphone app to log and process the claim. One US insurtech is even using facial recognition technology to help validate claims, while, closer to home, a UK home insurtech is using digitised home contents inventories to reduce the risk of underinsurance and streamline claims.

This unique perspective comes from a starting point of “what’s in it for the customer?”

As insurtech propositions rely on leveraging their customer’s data, they need to build trust from the ground up through complete transparency in their processes. As part of this, they are also educating their customers, helping them understand why insurance is important and how they can add value to the customer’s life. They are helping to change the basic concept of insurance, from a grudge purchase to a precious commodity.

Paul Middlege, Davies: Insurtech companies along with regular insurers, have a wealth of data about their customers. Insurtech companies have the advantage of having less dependencies on archaic legacy systems so easier access to the data they hold

– many will use ‘easy access’ cloud-based systems as their primary customer database / data set.

Insurtechs can also take faster advantage of IOT technologies to integrate new data sources into their systems. The combined scenario of coupling easy access customer data, sources of new data from IOT with powerful analytics solutions means the data can be used in new and interesting ways. We see a number of common examples in the market today such as more targeted risk profiling when it comes to pricing decisions.

If data tells us a particular house or a set of houses on a street is less prone to flooding versus other houses on the same street, damage cover could be offered where previously unavailable or premiums set at a different rate depending on the specific risks of individual properties – something unheard of with postal district and water table analysis. Or if IOT devices can be fitted to gather new data, the most prevalent use

case for this would be black box telematics in vehicles for young drivers, then again premiums can be personalised based on individual customer attributes. The analysis of unstructured data is now another growing data set being used in this space – e.g. being able to identify key customer or risk attributes in voice and text-based interactions.

One area we are seeing evolution in, is using predictive analytics and process automation to improve business processes and improve the customer experience. One particular use case is the automation of low value, low risk claims. Data can be captured from FNOL interactions (voice calls, emails etc) and can be screened to identify a number of attributes – some examples include; detect reason for claim, identify if any third party involvement, identify any fraud indicators, identify any customer vulnerability triggers etc.

Then if the claim is deemed ‘low risk’