Published : 4 years ago, on 22/02/2021

Global Banking and Finance Review recently caught up with John Beal, Senior Vice President, Analytics, Insurance, LexisNexis Risk Solutions to understand how data and analytics are shaping the future of the insurance industry globally.

1. They say data is the new gold. Explain this in the context of the insurance market and how this has impacted your business.

The insurance industry has become more competitive every year and those insurance companies who can improve their business practices through the use of data and analytics will be the winners.

Insurance providers are constantly evaluating new data driven solutions to better segment their customers than their competitors. Added to this, many providers are investing in their own analytics capabilities – this is more advanced in some countries than others. This creates an unending appetite for more and more data, attributes and scores. These factors and the inherent untapped value of our data assets has fueled our growth over the past several years.

2. You must have a sizeable team to meet this need? 

Today, there are over 130 people across various geographies including Europe, Brazil, and China.  We continue to invest and expand our footprint into more markets and we expect to continue to grow as the demand for analytically derived products increases globally. We continue to research new areas of opportunities in more mature markets, which would lead to organic growth with new product offerings and data analytics initiatives across the organisation.

However, the size of the team is not as important as the breadth of capabilities we bring to our markets. It is not enough to be able to build new predictive solutions, you need to be able to put new concepts into production quickly and efficiently. In addition, insurance providers need to understand relevant benchmarks to understand how they are performing and where they need to invest.  The team offers the range of skills needed to meet these demands.

3. How has your team structure changed in response to the increasing demand for data insights from the market? 

Overall, our objective is to continuously adapt to the ever-growing demands of our insurance customers by proactively providing actionable, data-driven insights to the market.

We have dedicated Analytics teams that support specific lines of business. Certain product lines require the focus of a dedicated Analytics team because the data sources and processes are highly specialised – Vehicle Build, which is a solution to better evaluate specific Advanced Driver Assistance Systems (ADAS) to support pricing and underwriting, for example. We also have several teams that provide support to these analytics teams and the business. The Analytics Batch team produces hundreds of test files each year for our customer to validate the value of our solutions.

In addition, we anticipated the industry’s demand of analytics coming several years ago and created the Attribute Development Team, responsible for operationalising thousands of new attributes each year to feed the industry’s data appetite as well as supply our internal data scientists with a constant source of new data points in order to create new products.

As we manage hundreds of complex predictive models and tens of thousands of attributes internationally on a daily basis, implementing and maintaining the highest quality and consistency possible is vital to our business. The Analytics Audit team manages this at a global level and constantly looks for process improvements across all of our product implementations.

4. How do you attract and retain the right skills to your team? Aren’t data scientist in short supply?

Today, almost every industry leverages analytics and is competing for data scientists.  So, the demand for Data Scientists has increased every year. Universities and Colleges are expanding the number of programmes across the globe, but I think there will be a supply shortage for a long time.

To attract talent, we are fortunate to offer what every data scientist dreams about, data. Our data scientists work with literally hundreds of millions and often billions of records to solve our customers’ problems. Many other companies are very limited in the breadth and depth of their data and many lack the ability to pull it all together in a commercially viable application. We do that every day and that is exciting to a candidate.

Our Data Science Rotational Program (DSRP) sees recently graduated data scientists join LexisNexis for a two-year cycle through four different teams. This experience provides a robust hands-on journey from data access, data analysis, model building to model implementation. Right now, we have seven DSRP team members in this programme and we typically hire three new positions each year.

5. What has been the most exciting development in the past year?

It’s tough to pick one but the Vehicle Build product is a global solution and being tested across the US, UK and European markets today.  We were able to develop a robust product due to the high quality, advanced analytical work that the team undertook.  They took the time to understand the intricate details associated with ADAS features and technology equipped on a given vehicle.  This product has uniquely positioned us to serve the needs of our clients by offering VIN-level insights.

LexisNexis Risk Solutions has logically sequenced and classified hundreds of variations of vehicle safety features and components into a common taxonomy. In doing so, we are enabling insurance providers to more easily ascertain how these features influences a vehicle’s risk profile. These insights can then be incorporated into pricing and claims workflows.

6. Can you let us in on the team’s focus going into 2021?

Geospatial analytics is an area we think will provide a number of new predictors into our modeling applications. Image recognition is another area we will be looking at and of course, vehicle build attributes and scores hold a lot of promise globally in both pricing and claims areas.  Trends and benchmark reporting has been under a spotlight with the impact of COVID-19, but we see this area expanding across the globe.

7. What are the big questions coming from customers today?

Adoption of more data and analytics is the competitive advantage Insurance providers are focused on today. They want more attributes for their data scientists to evaluate. They want the ability to test data faster and with larger files so they can make quicker decisions.

The demand for more cloud support is growing. As insurance providers move their own systems to the cloud, they need data and analytics delivered seamlessly.  They are always looking for ways to save expenses while minimising impact to their risk exposure.

8. How does the process of creating a new data solution work?

If the concept works and the market opportunity exists, we create the final specs for technology to implement. Before the final implementation, our attribute team is involved in order to create the attributes or inputs into the solution and our audit team works with technology to ensure the final product performs as expected.

While this development work is happening, a testing strategy is developed for customers. We may create actionable insight studies or perform retro validations tests through our batch team. The goal of this work is to demonstrate the value of our solution on the insurance provider’s own data.

As we get closer to product launch, we work with our product team to support any required regulatory documents on the solution inputs, outputs and overall performance. Once the product is in production, the batch team continues supporting the validation process for new customers. Finally, we monitor the attributes and scores to ensure they continue to perform as expected. If we see any issues, we work with Product and Technology to address the issue. At some point, the product will need to be redeveloped which means rebuilding the solution under the direction of the Product team. Once that process starts we start the cycle all over again!

9. What are the biggest misunderstandings/misconceptions about data analytics within the insurance sector?

A big misconception in the industry is that big data and analytics driven products will replace human capital. Data-driven products allow insurance companies to streamline their existing processes and are meant to complement their existing workflow. Human judgment and expertise will always be required to accurately price risk in line with a company’s business strategy. However, data-driven insights can assist with the decision process.

10. What are the biggest barriers to successful modelling/data analytics and how do you envisage they should be solved?

The time it takes to implement or operationalise an analytics solution. There’s always needs to be a balance between availability and accuracy to ensure the product produces the expected outcomes.  A natural consequence of technology’s ability to deliver solutions quicker each year creates a challenge to continue to find ways to expedite our processes.

11. What trends do you see in the application of data from the IoT for insurance? 

We do see very positive trends in the application for some IoT devices. We have partnered with several home IoT manufacturers and to validate reductions in home insurance claims – both the frequency and severity due to the presence of the device. Also, with more people at home during the pandemic, we’ve seen the severity of home insurance claims reduce. For example, if an IoT escape of water alarm goes off, being there in person to shut off the water can stop an insurance claim becoming very expensive.

 However, gathering enough performance or claims data to validate the value of any individual IoT device is an ongoing challenge. It takes time for a device to become widely distributed and the data centrally collected. Once that happens, we do expect to see a need to standardise and normalise the data collected by the many different devices that are in the marketplace today. The good news is we have been in this business with telematics devices for many years and we have extensive experience creating device generated attributes and scores.

12. Are there specific data trends you see emerging through the Covid19 pandemic in terms of how insurers can prepare for future risks around pandemics?

A significant challenge for insurance providers and our own business is any sudden change in consumer behaviour. We all do things we don’t even think about as part of our daily routines.  Shopping for insurance, driving to work and going to the supermarket are just a few activities that just happen. When any of these things stop, insurance providers need to expedite their ability to service their prospects and customers virtually. This includes prefill solutions, data driven underwriting, pricing applications, and contactless claims processing. Fortunately, we have been developing these solutions for years and are in the best spot to help insurance providers interact effectively with their customers.