Role of Underwriter in Age of Insurtech

Automation and machine learning need to be force multipliers for underwriting excellence – not poor substitutes for it.


The age of insurtech has brought a wave of new digital experiences and automation in insurance. From websites that instantaneously compare auto insurance quotes to mobile apps that allow us to submit claims directly by snapping a picture of a damaged window, we continue to benefit from significant improvements to the insured experience.

These improvements in distribution and claims are part of an industry-wide appetite for increased accuracy and efficiency, including in underwriting. Personal lines carriers have already made good strides, and carriers see a similar opportunity to improve loss and expense ratios in commercial lines.

For small business policies that involve a high volume of submissions and lower premiums, the challenge is to enable an efficient, high-throughput underwriting process that complies with exacting standards for quality. In the mid-market, the stakes are even higher for underwriters. They must be diligent about selecting high-quality risk against a backdrop of declining capacity and a tsunami of submissions from brokers who remarket risks in search of better rates.

While the goal of shorter time-to-quote is laudable, and addresses a critical frustration for insureds and brokers, the implementation often overlooks the crucial role that underwriters play. By failing to listen to underwriters' needs and play to their strengths as expert assessors of risk, technology providers and insurers alike continue to achieve sub-optimal underwriting outcomes.

Commercial underwriters are at the forefront of some of the most challenging and important work in the industry. They serve a multi-faceted role: developing and fostering relationships with brokers, exhaustively reviewing submissions, validating an insured's business and property information, analyzing exposures and, eventually, rating, quoting and binding policies. Underwriters must bridge the gap between carriers that set aggressive goals for profitable premium growth and brokers who want a quote "yesterday" -- and often pair incomplete submissions with demands for a rapid turnaround.

When underwriters conduct a thorough investigation of the risk – executing online searches, ordering inspections and asking tough questions, they’re invariably perceived as being too slow, inflexible and uncooperative. If they compromise on thoroughness to increase throughput, or if too many submissions are superficially passed through, their book may grow quickly, but the quality and profitability will suffer. All the while, underwriters want to deliver a comprehensive policy that best addresses the insured’s needs and grows the relationship. Reconciling these often-conflicting priorities is difficult but sets the most effective and experienced underwriters apart.

Data analytics, artificial intelligence and machine learning can make a big difference but, for most insurers, have failed to deliver great value within underwriting.

Improving outcomes requires an approach that combines the best of underwriter judgment with machine intelligence.

See also: The Future of Underwriting

Specialized, AI-powered software can now do much of the heavy lifting for underwriters, while eliminating frustrating activities. Underwriters who experiment with, and embrace, new technologies are already setting themselves apart from their peers. They stand to improve their individual performance and also help to chart the future course of underwriting within their organizations.

For insurtechs to truly deliver on their collective promise, they need to empower those who are actually performing the work of insurance. Automation and machine learning need to be force multipliers for underwriting excellence – not poor substitutes for it. Getting this right will lead to a better experience for the insured and superior outcomes for the industry.

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