The insurance industry has always been driven by data. Today, insurance underwriters are under the gun to use new data technologies to shift from hindsight-dependent to future-ready processes. These technologies are unproven and imply risk, but should we be concerned? 

Underwriters have primarily relied on historical data to predict tomorrow’s risk. In a world with climate change, inflationary pressures amid global economic uncertainty, and increasingly complex supply chains, life is becoming less predictable. Can new technologies such as AI, machine learning (ML), IoT, and data collaboration bridge the gap to help us assess unknowable risks? If we use yesterday’s standards, such as actuarial tables, will we be caught off guard?

Naturally, more data and sophisticated methods of translating that data should equate to better decision-making, but we can’t depend on new techniques and algorithms to do all the work of predicting risk. Underwriters have their work cut out for them when it comes to adapting—and predicting—a more volatile and data-rich world. A team of sophisticated data engineers must look through the information and make a final decision—enter the underwriter. 

“Underwriters, if they’re doing their job right, are the most honest people in the room,” says Jules Rochman, Founder of the Insurance Institute for Business and Home Safety. “They don’t care about the politics, they just care about the risk. A good underwriter looks at risk and says ‘What can we engineer away? What can we mitigate and prevent?’ They bear the risk for what’s left.” 

Understanding “what’s left” is a complex dilemma for underwriters, and their interpretations carry serious implications for  an insurance carrier’s financial health. 

More data, more complexity 

In addition to traditional sources like actuarial tables, insurance carriers are starting to incorporate third-party data sources such as wearables collecting health metrics, telematic devices measuring driving behaviors, and GIS mapping and satellite imagery. More often than not, the potential of this data sits frustratingly out of reach for the majority of underwriters. A 2022 Accenture survey found that “up to 40% of underwriters’ time is spent on non-core and administrative activities.” Of those surveyed, 64% said the use of technology has made no difference, or has even increased their workload. 

“I think the risk of incorporating new data is the same as it’s always been. The more pipelines of data you have coming in, the more confusing it is,” says Rochman. As the industry works to adapt to these new data technologies, there are industry challengers already taking advantage. 

Companies such as Lemonade and Hippo are using AI-powered chatbots to personalize customer experiences, IoT devices to monitor and prevent problems before they happen, and ML to streamline quotes and claims processes. Valued at a modest $5.45 billion in 2022, the Insurtech market is slated to grow to $152 billion by 2030.

The minority of underwriters able to take advantage of new data technologies are liking what they see. A recent study from Accenture found that tech-enabled underwriters can:

  • Produce quotes faster
  • Handle more business
  • Access more valuable knowledge across their organization 
  • Accomplish more with ease 
  • Effectively rate and price risk better than before 

While it may be easy to ask why everyone isn’t capitalizing on new data tech, the reality is that a majority of the industry is still playing catch-up. 

Modern data requires modernized data ecosystems

Are major carriers behind the times? Sully McConnell, Head of Insurance at Snowflake, offers some insight: “Traditional insurers enjoy very large volumes of data, but typically have older systems that impede their ability to evolve quickly in order to support the new approaches and technologies we’re seeing today. Carriers have to be careful when thinking about evolving those systems in order to support new technologies being intermixed with their existing platforms.”

As a result, technology debt and legacy systems place most underwriters in scattered environments that are rife with disorganized data. When underwriters have new tools, getting them to work within existing workflows can take just as much time as they’re supposed to save. When significant investments have been made in legacy systems, it can be harder to see the ROI of modernization. 

Does this mean major carriers are at risk of being usurped by new players? It depends. 

“I think it’s fair to say that the C-Suite of almost every insurer believes they need to compete on data and analytics,” says McConnell. “Many organizations are in the early stages of moving on-premises systems to the cloud, and trying to simplify and modernize their platform in the process. There’s a lot of momentum around really trying to take advantage of all the capabilities that the cloud has to offer.”

The transition may be difficult, but moving to a cloud-based system comes with a lot of benefits:

  • The ability to scale elasticity without investing in new hardware or software
  • Flexibility to share and collaborate across an entire organization
  • Security and governance features that enable protection of sensitive data (such as PII) for businesses and consumers
  • Easy integration of innovative features and technologies
  • A cost-effective alternative for carriers to pay for the computing as needed

As new technologies shift the industry toward modernization, the underwriter will have to evolve as well.

How data will shape the underwriter of tomorrow

Deloitte calls this the exponential underwriter—a multi-skilled professional leveraging new data sources and emerging technologies to be more efficient and proactive in defining future organizational processes.

New tech means underwriters will have extra time to focus on high-value cases that require human insight. They will introduce, interpret, communicate, and defend their tech-enabled decisions to stakeholders while working with leadership to improve the efficacy of business intelligence. This has the potential to fragment the role of the underwriter, meaning that certain underwriters will mature into key areas of specialization. 

The pure scalability of these technologies should present opportunities for key wins in underwriting. Executives will insist underwriters deploy new technologies, such as low-touch straight-through processing (STP) models that require little if any manual intervention, to make their risk-assessment processes leaner. Others may specialize in putting data under the lens—creating, sharing and collaborating on models internally—to uncover cutting-edge metrics that set a new standard for the industry. 

For now, it’s anyone’s guess as to how quickly the underwriting profession will evolve. One thing is certain, however: new data technologies will have a huge impact on tomorrow’s underwriters. It’s up to today’s data executives to provide the best tools and insist on their use to deliver value. 

The more data we have, the better decisions we should make. And thanks to these exciting new technologies and the prudent executives implementing them, underwriting will only continue to improve and evolve.