Can AI Help Reduce Underwriter Fatigue?

Carriers would be to begin incorporating AI into their existing systems, as AI tools have been shown to be highly effective at alleviating work pressures through automation.

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The insurance industry relies on accurate underwriting to remain profitable, and underwriters have always relied on data to do their jobs. This has been true ever since the first insurance policy was written and with the rise of big data underwriters now have access to more data than ever before. However, to my mind at least, the end result of this data explosion has been something of a mixed bag.

On the one hand, underwriters now have more data for evaluating and pricing policies. On the other hand, increased data access also hasn’t necessarily changed the underlying issue that many underwriters are still saddled with legacy systems that make their roles labor-intensive and time-consuming. If anything, higher data volumes have actually increased the workload for underwriters and contributed to large-scale fatigue that is adversely affecting the survival of the industry.

Obviously, more needs to be done to reduce the burden on underwriters. My advice to carriers would be to begin incorporating AI into their existing systems. AI tools have been shown to be highly effective at alleviating work pressures through automation. Yet, as I’ll explain below, automation is only the starting point.

AI in underwriting

AI is a super-hot topic now in business circles and a lot has already been said on how it can reduce work burdens and boost efficiency. You’ve probably heard it all already, how it can automate various underwriting tasks, reduce human errors, and drive more consistent and fairer risk assessments. These are all immensely beneficial advantages and I fully believe that AI will become an integral part of underwriting core processes within the next decade.

And yet, task automation is only the tip of the iceberg when it comes to optimizing insurance underwriting through AI. The way I see it, carriers need to go deeper when leveraging these tools by looking at more innovative ways to use them in the underwriting process.

Take data collection as an example. An underwriter’s primary task is to assess each applicant’s insurability, which they can’t do if any critical data points are missing or in need of clarification. Typically, an underwriter would have to email the agent and ask them to source the required information from the applicant. From what I’ve seen, agents will typically sit on these emails for weeks before finally gathering and sending the necessary information. And once that’s done, further days or weeks can then pass before the underwriter opens the email.

I think I speak for a lot of insurers when I say that this back-and-forth process is extremely inefficient and in need of change. What’s important here though is that underwriters can use AI-powered chatbots to clear up this data-collection muddle. These chatbots can open a real-time conversation with applicants, tell them what data is needed, and explain why the data is important. This could potentially reduce the time to complete an application by weeks, with some setups even allowing for automatic data entry and policy approval so long as the application is within certain risk factors.

That brings us to another use case for AI that carriers should take more seriously; the ability to fully automate some if not most of their underwriting. A typical underwriter will have a mountain of applications to get through on any given day. Often, they’ll open an application that’s been on their desk for weeks only to see that it’s either laughably easy to underwrite or well outside their risk allowances. These sorts of applications are a drag on experienced underwriters, whose time would be better spent on the more complex and nuanced applications.

Instead, underwriters should turn these applications over to an AI program that can autonomously decide on an application’s insurability based on given inputs. This is relatively easy to set up and works by assigning a risk score to each application. For example, a score of between one and four might be considered high risk, resulting in an immediate rejection. A score of between five and seven would be moderately risky and flagged for review by a human underwriter. Anything above eight would be low risk and receive immediate approval.

Final thoughts

AI is the future of insurance, and as time goes on more carriers will begin to view this technology as a necessity. Yet as I’ve argued above, carriers also need to take a holistic approach to how they can fully utilize AI. Not every use case will be blindly obvious from the start and when it comes to underwriting, the less obvious use cases will likely have the biggest effect.

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 Bob Gaydos // Bob Gaydos is the Founder and CEO of Pendella where he leads a team of innovators in the insurance industry, automating the underwriting process through AI and big data. Over the last 10 years, Bob has founded, invested, advised, and operated innovative companies in the benefit & insurance industry, such as Maxwell Health: an online benefits administration platform acquired by Sun Life in 2018. Connected Benefits: an online insurance agency acquired by GoHealth in 2016. Limelight Health: a group underwriting platform acquired by Fineos in 2020. GoCo: an online platform for HR, benefits, and payroll. Ideon (formerly Vericred): an innovative data services platform powering digital quote-to-card experiences in health insurance and benefits.

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