AI deployment, development and other issues in the insurance industry

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The insurance industry has not always been the quickest to change and adopt new, more streamlined ways of doing things. Cumbersome paperwork and manual processes have burdened insurers for decades, but the industry is finally ready to embrace new technologies like artificial intelligence to improve efficiency and client satisfaction. 

The potential benefits are clear. AI helps insurers accelerate underwriting and claims processing by analyzing historical data to evaluate risk. Insurers are also using AI to detect fraud and to generate accurate loss reports. As a result, the effective use of AI can improve claims accuracy by up to 99% and increase efficiency by around 60%. That means customers get policies settled and claims approved faster, resulting in a greatly improved customer experience. 

Some of the more visible uses of AI in health insurance include chatbots, with software like ChatGPT and Google's Gemini helping them become more understanding of the needs and complaints of customers, and enabling insurers to offer more personalized assistance on their websites. A chatbot could even guide a customer through the steps of submitting a claim, including what documentation the customer needs to include.

Read more: How AI is reshaping insurance underwriting

Deployment, however, remains an issue. While many organizations are using and testing various forms of traditional and advanced artificial intelligence, including machine learning, deep learning and generative AI, most AI projects fail to reach deployment, according to Eric Siegel, a former professor at Columbia University and data scientist.

Siegel has had a lifetime obsession with predictive analytics and AI — so much so that he wrote and performed a music video about predictive analytics and has written a book called "The AI Playbook" – and is committed to changing this.

"I'll do anything to help educate and ramp up the world on this technology," Siegel recently told Digital Insurance's sister publication, American Banker. "It's fascinating learning from data to predict and then use those predictions to improve any and all of the large-scale operations that make the world go round."

Read more: Predictable tasks are made for today's generative AI

Siegel's optimism is tempered somewhat by the need for insurance companies to watch the latest industry regulation on where responsibility lies for using AI for underwriting and pricing. New York state recently issued new regulatory guidance on the subject, one of the first states to do so.

New York's guidance came from its Department of Financial Services, which regulates insurance, in the form of a circular related to Insurance Law Article 26. This is state law that addresses unfair claim settlement practices, discrimination and other misconduct, including making false statements. The circular specifies that the elements the law addresses should not be violated by the misuse of AI and consumer data and information systems.

Catch up on this and all of our recent coverage of how AI is reshaping the industry.

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New York Insurers are responsible for their use of third-party AI

Insurance carriers in New York state must shoulder the responsibility for using third-party AI technology, according to a circular issued by the state's Department of Financial Services, which regulates insurance. The circular is related to Insurance Law Article 26 which addresses unfair claim settlement practices, discrimination and other misconduct. The circular specifies that the elements the law addresses should not be violated by the misuse of AI and consumer data and information systems.  

"If you're using third-party systems, you cannot punt the accountability to the third party," Karthik Ramakrishnan, co-founder and CEO of Armilla, an AI model and verification technology company that serves the insurance and other industries, told Digital Insurance Senior Editor Michael Shashoua. "The insurer is still accountable for the end outcomes and that's what the circular really tries to emphasize." 

Read more: NY insurance regulator makes carriers accountable for third-party AI systems
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Lack of deployment planning holds back AI initiatives

Targeting marketing, fraud detection, credit score management, insurance and pricing and selection, are just some of the application areas that could benefit from advanced AI, including machine learning, deep learning and generative AI — but all too often AI projects are never actually deployed, says Eric Siegel, a data scientist and former professor at Columbia University.

"We need to bridge a gap between the buzzwords and the tech, and bridging that gap requires business professionals to ramp up on a certain semi-technical understanding so they can collaborate deeply in a meaningful way," Siegel told Penny Crosman, executive editor of Technology at American Banker. "Right now, most new enterprise machine learning projects actually fail to reach deployment and it's due to this gap and a lack of rigorous business side deployment planning."

Read more: Most AI projects fail to reach deployment: Eric Siegel
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Risk vs. reward debate continues for use of AI in health insurance

In 2017, just slightly more than 1% of insurers were using AI, compared to 30% in many other industries. Today, experts estimate that AI in insurance will be worth $35.77 billion by 2030, with a compound annual growth rate of more than 30%.

"Unfortunately, it hasn't been an entirely smooth transition to AI automation in health insurance," Stephen Dean, co-founder of Keona Health, recently wrote in a column for Digital Insurance. "Recently the families of two deceased UnitedHealth policy holders are suing after the company allegedly used a faulty AI that denied elderly patients coverage for medically necessary care. The American Medical Association also warned of potential risks for health insurance companies using AI and is now advocating for greater regulatory oversight."

Dean explores how carriers are using AI in their day-to-day business, what problems and challenges they are encountering, and where the future lies for AI in the industry.

Read more: Is integrating AI and automation in health insurance worth the risk?
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How insurtechs can get their AI message across to the industry

AI may be the new kid on the technology block, but the messaging challenges it presents to insurtech leaders are familiar. The industry has always had to move steadily toward the horizon of new tech while finding ways to encourage adoption among traditionalists.

Josh Inglis, founder and CEO of Propllr, attended the Insurtech Connect conference in Las Vegas last October and believes that Henriette Fleishmann, CEO of virtual property assessment firm Hosta, captured the challenge best: "We are operating in an industry that is massively risk-averse, and that's for a good reason. But AI in life and business will soon be like electricity – essential, invisible, ubiquitous."

In a recent piece for Digital Insurance, Inglis shared insights from conversations with insurtech marketers and founders about their use of artificial intelligence. The biggest takeaway was that, while AI is disruptive and exciting, it's also just the latest technology to present a delicate but familiar messaging challenge: manage the tension between the risk-averse stakeholders on the "insure" side of the industry and those with a much greater appetite for uncertainty on the "tech" side.

Read more: For insurtechs, AI is the latest wobble of a familiar tightrope
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Managing the ‘gold rush’ in AI development for insurers

During the California gold rush, while many people focused on mining for gold, others recognized that providing crucial tools and equipment for prospectors was equally important. These entrepreneurs became known as "picks and shovels" providers and were vital for prospectors in their search for gold.

Today's equivalent entrepreneurs in AI for insurance are those who recognize how important data annotation and model training are to realizing the promise of AI, Martin P.D. Henley, CEO of Mea, explains in a recent column for Digital Insurance. "Just as the picks and shovels providers supported the search for gold, companies offering essential annotation services used for training the AI models of Open AI, Microsoft and others have become the backbone of AI development."

While there are many challenges to be overcome, Henley discusses how for insurers, and especially commercial insurers, the key to unlocking this transformative opportunity is the ability to extract and structure data from unstructured communications, documents and other types of digital content, accurately, reliably and cost effectively.

Read more: Beware of the risks surrounding AI training