The Future of Insurance is AI 4 Ways to Embrace this Change

April 13, 2021

What exactly is AI?

In 1950, Alan Turing asked the question, “Can machines think?” And, at its core, that’s what artificial intelligence (AI) is all about. It’s the attempt to replicate human-life intelligence in computers, machines, and even robots.

At the same time, not everyone can agree on a uniformly agreed-upon definition — we could thank Hollywood for that. However, I feel that Darrel M. West sums it up nicely the Brookings Institute;

Today, AI generally is thought to refer to “machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment, and intention.” According to researchers Shubhendu and Vijay, these software systems “make decisions which normally require [a] human level of expertise” and help people anticipate problems or deal with issues as they come up.

Hopefully, that gives you a clearer understanding of what artificial intelligence is. As for how it’s being used, it’s typically applied to two broad categories;

Narrow or Weak AI. Despite its title, this type of AI is anything but weak. It’s called this because it focuses on performing a specific task extremely well. Voice assistants like Alexa and Siri to self-driving cars are examples of Artificial Narrow Intelligence (ANI).
General or Strong AI. This is the type of AI that we frequently in pop culture, think HAL in 2001: A Space Odyssey or the robots in Westworld where machines attempt to replicate the human brain in order to solve problems on their own. However, this is more theoretical and there are no practical examples. So, you can sleep easy tonight knowing that a dystopian future isn’t likely — at least any time soon, but that might be beneficial for the industry.

It should also be noted that there two subsets to AI.

The first is machine learning. As you might have guessed, this when computer systems “learn” so that they can make more accurate predictions. Of course, they don’t do this completely on their own. Rather, they’re feed data and use the information to provide recommendations on what to watch on Netflix to predicting when a patient will get sick.

The second is deep learning. This is a class of machine learning that reprograms itself to perform a task more accurately. Chatbots, virtual assistants, and autonomous cars are several examples of deep learning.

Where does AI meet the insurance industry?

Even though AI has real-world implications, how has it specifically changed the insurance industry? Well, here are seven AI applications to the industry space.

Pricing. With AI, insurers can offer more competitive and personalized pricing for their customers based on data like biometrics, location, user-generated health records, driving records, and financial situation.
Claims handling. Processing claims can be costly and time-consuming due to manuel/inconsistent processing, varying data formats, changing regulations, and case-solving. AI can automate most of these tedious tasks, provide real-time information, and more accurately process claims.
Compliance and risk management. AI can do more than just automate repetitive tasks. Insurers can use insurtech from companies like TrustLayer to automate compliance and mitigate risk by verifying coverage. AI can also be helpful in improving predictive and risk analytics and identifying potential cyber risks.
Document creation. Generating higher volumes in documents isn’t just time-consuming. It can also result in costly mistakes. AI will process received documents and create policy statements to reduce costs and errors.
Responding to customer inquires. Chatbots, for example, are being deployed for customer segmentation. They can also deliver fast and efficient answers around-the-clock.
Personalized services. Customers demand a more personalized experience these days. In fact, Accenture reports that more than 80% of insurance customers are looking for more personalized experiences. AI can make this possible by offering customized products and plans so that they’re only paying for the coverage that they need.
Fraud detection. According to the FBI, the “total cost of insurance fraud (non-health insurance) is estimated to be more than $40 billion per year. That means Insurance Fraud costs the average U.S. family between $400 and $700 per year in the form of increased premiums.” AI, as you’ve correctly assumed, has the ability to detect fraud and avoid these risks.

How insurers can prepare for accelerating changes.

Make no mistake about it. Artificial intelligence is here. And, it’s already disrupted the industry. But, as it becomes more accessible and technology advances, insurers must be willing and prepared for these changes.

While this may seem daunting and overwhelming, particularly if you’re not tech-savy. The good news is that McKinsey recommends that you focus on the following four areas. Even better? Any insurer, regardless of your familiarity with AI, should be able to easily implement these.

1. Get smart on AI-related technologies and trends.

Let’s be clear. When it comes to AI, this should not be solely the responsibility of your IT team. “Instead, board members and customer-experience teams should invest the time and resources to build a deep understanding of these AI-related technologies,” advise Ramnath Balasubramanian, Ari Libarikian, and Doug McElhaney for McKinsey. “Part of this effort will require exploring hypothesis-driven scenarios in order to understand and highlight where and when disruption might occur—and what it means for certain business lines.”

In other words, don’t be afraid to play around and educate yourself with AI technology. At the same time, you also need to understand its purpose. One example from McKinsey would-be pilots and proof-of-concept (POC) projects. In addition to testing the tech out, they should also evaluate “how successful the carrier might be operating in a particular role within a data- or IoT-based ecosystem.”

2. Develop and begin implementation of a coherent strategic plan.

After collecting insights from your AI exploration, you need to determine how it fits into your overall business strategy. “Insurers should develop a perspective on areas they want to invest in to meet or beat the market and what strategic approach—for example, forming a new entity or building in-house strategic capabilities—is best suited for their organization,” the authors recommend. Ideally, this should address the four dimensions that compromise a large-scale, analytics-based initiative;

Data capabilities
Organization and talent
Change management
Models and tools

Also, carriers should schedule milestones and checkpoints to track progress and make adjustments. Additionally, insurers should “develop strategic responses to coming macrolevel changes.” At the forefront of this would be adopting a “predict and prevent” methodology to modify customer engagement, personalized plans, and branding.

3. Create and execute a comprehensive data strategy

For AI to be effective, it requires mass consumption of data. Carriers can then use this information to determine, calculate, place and manage errors/risks throughout a policy’s life cycle. As such, there needs to “a well-structured and actionable strategy with regard to both internal and external data.”

“Internal data will need to be organized in ways that enable and support the agile development of new analytics insights and capabilities,” the authors explain. “With external data, carriers must focus on securing access to data that enriches and complements their internal data sets.”

Be aware of possible speedbumps, such as security and cost-efficiently. Moreover, “be prepared to have a multifaceted procurement strategy that could include the direct acquisition of data assets and providers, licensing of data sources, use of data APIs, and partnerships with data brokers.”

4. Create the right talent and technology infrastructure.

“To ensure that every part of the organization views advanced analytics as a must-have capability, carriers must make measured but sustained investments in people,” state the authors. With that in mind, insurers can prepare for the future by surrounding themselves with the right talent who possess “the right mindsets and skills.”

“The next generation of successful frontline insurance workers will be in increasingly high demand and must possess a unique mix of being technologically adept, creative, and willing to work at something that will not be a static process but rather a mix of semiautomated and machine-supported tasks that continually evolve,” they add.

While this will be beneficial for insurers, it will take “a conscious culture shift for most carriers that will rely on buy-in and leadership from the executive suite.” What’s more, it will take new strategies “to attract, cultivate, and retain a variety of workers with critical skill sets will be essential to keep pace,” such as “data engineers, data scientists, technologists, cloud computing specialists, and experience designers.”

As for those who aren’t in these fields, anticipate a reskilling program to become at least somewhat familiar with the technology.

Final thoughts.

If you want to thrive in the insurance industry, then you can’t avoid artificial intelligence. It’s too prevalent and disruptive. But, if take the appropriate steps today, then you’ll be well-prepared for tomorrow.