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Founder Shield: Understanding How AI and Machine Learning Impacts Underwriting

Automated underwriting has become more widespread in recent years. However, this practice is undergoing a transformation that may change the insurance industry forever.

Ryan Jeziorski, Executive Vice President at Founder Shield
Kyle Jeziorski, Executive Vice President at Founder Shield

Here risk management expert Kyle Jeziorski reviews how AI and machine learning (ML) are increasing automated underwriting’s popularity and what to expect in the future.

Jeziorski is Executive Vice President at Founder Shield, a New York-based tech-enabled commercial insurance broker.

Automated underwriting’s success story

Many insurance experts view traditional underwriting as an antiquated process, requiring real humans to evaluate the risk involved with particular business operations. As you can imagine, the number of workers necessary to evaluate every business risk is astronomical, not to mention the hours it would take to do such a task. It’s strange to think that this strategy was the only way to underwrite in the not-so-distant past.

Automated underwriting is a tech-driven process, using algorithms to make more accurate underwriting decisions. We now enjoy this innovative process to streamline risk management tasks in the modern world.

For example, consider submitting a credit card application and hearing back from the lender within minutes. An actual human isn’t sitting there eagerly awaiting the application submission. Instead, automated underwriting makes this instantaneous process possible.

It’s no surprise that the insurance industry has also adopted similar automated underwriting processes. Such software is more than a solution to lightning-fast responses; it’s a way to imagine a different future for the insurance industry.

The entrance of AI and ML

People shopping for insurance typically desire the most convenient and affordable policy, not to mention the quickest. What’s more, companies that use more automated processes are usually more competitive than those that don’t. So, it was a no-brainer to facilitate AI and machine learning into various insurance operations.

But let’s back up; manual or traditional insurance underwriting takes its sweet time. Regardless of the policy type, a personal or commercial underwriter must comb diligently through the client’s history (i.e., financial, medical, claim, etc.). Manual underwriting is a much longer process than its automated counterpart, and it also opens the door to more human errors.

In automated underwriting, AI software does the task of evaluating a potential client’s risk. However, AI and ML take this process a step further by deciding on a client’s coverage level and overall cost for the policy. This technology enables carriers to instantaneously accept or reject applications, not to mention how much the applicant should be paying for a policy. But that’s just for starters.

How AI and ML impacts automated underwriting

Automated insurance underwriting utilising AI and ML might seem like a dream come true for underwriters, especially those spending hours reviewing records. After all, the insurance industry has faced inconsistencies with manual underwriting techniques for years. Using AI and ML is one way to weed out the risky deals with clear-cut precepts in less time.

AI and ML work together with the carrier’s underwriting guidelines, providing a tailored approach for each insurance company. As a result, brokers and providers tend to profit more from automated underwriting because they can easily classify applicants.

Also, streamlining the process shaves time off of an underwriter’s job functions, allowing them to focus more on creating complex, high-dollar policies. Plus, automated underwriting enables carriers to provide quick responses to applicants, an aspect of business practices most of us have come to expect.

Still, we humans don’t merely let robots do all the heavy lifting. Most brokers prefer to keep a set of eyes on nearly every submission. For example, a typical Founder Shield client operates in an emerging industry and faces unique risks.

If we left the underwriting solely up to AI or ML, the client isn’t getting what they honestly need. However, these advancements create a beneficial filter to funnel a potential client in the right direction.

The future of underwriting In various lines

Some clients prefer the one-on-one attention from a dedicated underwriter throughout the insurance-buying process; however, it’s not beneficial to the client or broker. Plus, most clients have come to expect instantaneous responses from companies, including insurance carriers.

And since some underwriting data is quantitative, most carriers depend partially on the automated version.

We expect to see continued advancement of automated underwriting among legacy-players and insurtech companies. While most life insurance carriers have been using automated underwriting, we anticipate more commercial lines relying heavily on the automated underwriting process, making a commercial insurance broker’s role more effective.

.As mentioned earlier, most carriers prefer to triage insurance applications first thing. Skilled underwriters address any ‘red flags’ or unique situations as the application progresses. Automated underwriting works successfully as a sorting system, if for nothing else — but we see this process taking on a much more significant role soon.

Author

  • Tyler is a fintech journalist with specific interests in online banking and emerging AI technologies. He began his career writing with a plethora of national and international publications.

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