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Grow Your Business Technology

Artificial Insurance? How Machine Learning is Transforming Underwriting

Artificial Insurance? How Machine Learning is Transforming Underwriting
Credit: nito/Shutterstock

For an industry that has proven resistant to change for centuries, insurance is now undergoing a digital revolution. With the advent of more machine learning algorithms, underwriters are bringing in more information to better gauge risk and offer more tailor-made premium pricing. On the back end, the insurance process is being streamlined to connect applicants with carriers more efficiently and with fewer errors.

This drastic level of rapid change means big things for insurers and applicants alike. Here's how artificial intelligence, or AI, is on the frontier of the insurance industry and where it might be heading in years to come.

Historically, insurance underwriters have relied on information provided on applications to assess the risks surrounding a potential client. The trouble, of course, is that applicants could be dishonest or make mistakes, rendering these risk assessments inaccurate.

Machine learning, specifically natural language understanding (NLU), enables insurers to pore through more abstract sources of information, such as Yelp reviews, social media postings, SEC filings and so on, and pull pertinent information together to more adequately assess the insurance carrier's potential exposure.

"[With NLU] our ability to actually look at these textual data sources and pull out highly relevant information is greatly increased," said Andy Breen, SVP at Argo Digital. "We're making use of these information sources that weren't available or easily disseminated before."

More accurate risk assessments mean more appropriate premiums. In an industry where the largest difference between insurance companies is not their products, but their prices, a better, more individualized exposure model could make a big difference, said Sofya Pogreb, COO at Next Insurance.

"Traditionally, [the industry has offered] 'lowest common denominator' products: a standard liability policy," Pogreb said. "What you end up with is a very undifferentiated product, where a bakery and a laundromat have the same policy. That's not the right way to go for the customer. Being able to consume more data automatically, we will see more customization, and customers will benefit by paying for coverage they truly need."

Fraud is a major concern for insurance companies, and AI is a key watchdog in the fight against fraudulent claims. As Samsung notes in a blog post about insurance fraud prevention, it's all about detecting patterns that might escape human cognition:

"French AI startup firm Shift Technology incorporates this technology in their fraud prevention services, which have already processed over 77 million claims. The cognitive machine learning algorithms have reached a 75 percent accuracy rate for detecting fraudulent insurance claims. The ML algorithms provide details on suspicious claims with potential liability and repair cost assessments and suggest procedures that can resolve and enhance fraud protection."

"The ability of machine learning to assist in spotting suspected fraud is well established, but human-led data science is just as capable so far. The key difference over time will be one of cost," said Areiel Wolanow, managing director at Finserv Experts Limited. "Professional criminals will keep abreast of industry-leading fraud indicators and adapt their behavior to suit. Human data scientists will need to iterate their analysis over time to keep pace, while machine learning algorithms train themselves over time based on observable changes in the underlying data."

The distribution chain in the insurance industry is winding and complex. A series of middlemen examine information between the insured and the carrier, leading to a lot of human error and manual work that slows the process, said Breen. However, AI is already starting to fix that problem.

Algorithms can reduce the time and number of errors as information is passed from one source to the next. By logging into a portal and uploading a PDF, the amount of data entry and re-entry is reduced and accuracy is increased, Breen said.

"People get tired and bored and make mistakes, but algorithms don't," he added.

For Pogreb, bridging the gap between the insured and the insurer is as important as reducing error. With better data, both customers and insurers benefit, she said, because insurers can develop better products based on more accurate assessments, and customers will pay for exactly what they need.

"With machine learning, I think we'll be able to do a much better job giving the consumer that advice automatically," Pogreb said. "Based on what you tell me about your business and what I know about similar ones, [I can say] I believe this is the right combination of coverage for you. So it's putting the onus neither on the agent nor on the customer – who frankly doesn't have the experience or knowledge – but letting the data provide the advice."

The insurance industry has only begun its foray into AI, and companies are already experimenting with new ways to incorporate it into their day-to-day operations in anticipation of further technological development.

"It's the very early days of AI," Breen said. "For menial, repetitive tasks, we put the computer on it … but we're a ways away from a computer underwriter. We're really just augmenting humans at this point."

That's still a significant change in the industry, he said. Underwriters at Argo Digital are now beginning to manage portfolios, rather than review every single submission. The more standard, predictable claims are handled by machine learning algorithms, Breen said, and the human underwriter is essentially fine-tuning the entire process and intervening in cases that need higher-order decision-making.

Pogreb sees even more potential for streamlining the underwriting process. She expects that the number of applications a human underwriter will be required to handle will significantly drop as machine learning makes even more of a foray into the insurance industry.

"We believe with technology and machine learning, a lot of [human underwriting] can be done away with," Pogreb said. "The percentage of insurance applications that require human touch will go down dramatically, maybe 80 to 90 percent, and even to low single digits."

While adoption of AI has come in rudimentary ways, it's already drastically changing the lay of the land. Insurance companies that want to stay competitive should begin testing the waters of AI, Wolanow said.

"Companies can prepare and stay competitive by starting to assess the impact of machine learning on their business by prototyping their own algorithms," Wolanow said. "An individual machine learning algorithm that performs its analysis on a stand-alone basis is actually quite inexpensive, [and] in many cases, a stand-alone analysis tool is more than fit for purpose."

Adam C. Uzialko

Adam received his Bachelor's degree in Political Science and Journalism & Media Studies at Rutgers University. He worked for a local newspaper and freelanced for several publications after graduating college. He can be reached by email, or follow him on Twitter.