Top Data Opportunities for Life Insurers

 Top Data Opportunities for Life Insurers

The pace of data evolution is drastically different across industries.

CIO responses to research conducted by Celent shows that there is no part of an insurance organization that is untouched by data. However, although life insurers have a plethora of data on clients, going back even further than most industries, their ability to leverage that data to improve products, customer experience and decision-making lags.

By combining analytics, machine learning and tech solutions, insurers can benefit from the data they capture and create more value for their customers.

Here are the top 3 data opportunities that insurers can leverage to enhance their product offering and customer experience.

Eliminating data bias will give insurers more accurate insights into their customers and ensure more universal, nondiscriminatory coverage

Are our algorithms representing the underrepresented?

This was one of the most profound questions asked at this year’s InsureTech Connect Conference. Life insurance plays a central role in households’ financial security – and this is a big responsibility that carriers must own.

Every single person deserves to have protection. Every single person deserves insurance. So how do we do that? We need to do better.

One of the critical issues for life insurance companies is that, although they have huge volumes of data to work with, not all of it is relevant. Insurers need to be sure they’re collecting the right data.

In order for machine learning to be effective, you have to look at data over 10-20 years. What happens is that when you look, you see a lot of prejudices that were built into the data 10-20 years ago

Accepting diversity and mitigating any type of bias starts with the insurer and will benefit the insurer. Accurate data is key to uncovering true customer expectations and delivering a superior customer experience.

Eliminating data bias requires analyzing the entire process surrounding data collection and use. It’s important to verify the relevancy of metrics collected, as data bias can occur at any step in the process, from collection to analysis. For example:

What kind of information do you collect during the underwriting and claims processes? Are life insurance policies indirectly discriminatory or perpetuating systemic racism?

Discarded data – is it discarded because we don’t need it, or because we think we don’t need it?

Are your executives’ bonuses tied to customer satisfaction survey results? How do you think this impacts the questions put out there?

There’s a huge benefit to be reaped from being able to use data to create a sense of empathy in the marketplace.

Insurers have an opportunity to leverage data insights to provide customers with a more personalized experience

The next generation of life insurance customers is what we’re calling “The TikTok Generation”. More commonly known as Gen Z, research shows that this generation is much more willing to compromise on their privacy in return for higher efficiency and more personalized experiences.

While Millennials and Gen Z are concerned with the implications of losing privacy online, they’re much less concerned than older generations, especially if it can be traded for a more streamlined sales or service process.

Over the coming years, we are going to see this tension between privacy issues and how we can enable better customer experience

– Michael Levin, Principal, Total Insurance Inc.

Leveraging AI will enable insurers to uncover correlations that the human eye wouldn’t see

The evolution of technology means that insurers can now feed AI with alternative data sets and discern correlations to risk that humans wouldn’t see. This presents a huge opportunity to help refine the underwriting process – increasing efficiency, reducing cost, and increasing CX.

The growth of connected devices, wearables, and access to health data will play a large role in the personalization of life insurance offerings in the next ten years and will provide insurers with so much more data that can help uncover correlations to risk that teams of actuaries may never have seen.

Predictive, “what-if” modeling in insurance will allow insurers to prepare for the underwriting process. These kinds of analyses will help produce data for filings and evaluate the impact of a host of different types of changes on an insurer’s book of business.


Most insurers still don’t have access to all their data. This is because data is trapped in legacy systems and silos, leaving a lot of untapped potential.

The key solution to this problem is digital transformation and data migration. By migrating data from aging core systems and on to modern cloud-based platforms insurers can eliminate silos and make more data available for AI, BI, and analytics. They will be able to create much more insightful information that will help eliminate bias from the underwriting process, create a better customer experience and personalize product offerings.

Migrating data increases a company’s access to client information which helps them provide faster, more effective service, better assess risk and set prices, tailor marketing to more targeted groups and interact more frequently and more effectively with clients- all of which increases client trust and builds deeper relationships.

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