How artificial intelligence is driving change in insurance

Artificial intelligence has been a hot topic for several years. But the past year has seen a huge jump in the public’s awareness of AI as practical technology rather than science fiction.

What is AI? Microsoft has come up with a simple and clear definition: artificial intelligence is the capability of a computer system to mimic human cognitive functions such as learning and problem-solving. Through AI, a computer system uses maths and logic to simulate the reasoning that people use to learn from new information and make decisions.

AI technology has been steadily improving in capability and growing in scope. The insurance industry, for all its gigantic size and reputation for resistance to change, has made considerable strides in incorporating AI in various ways. AI’s potential is too great for it not to play an ever-increasing role in coming iterations of insurance technology.

The big AI news these days is of the free online beta platforms that almost instantaneously produce human-like results. DALL-E generates images from any text input. Type in“praying mantis riding a bicycle” and “ice cubes on fire” and the results are impressive but somewhat creepy. DALL-E, Midjourney and those like it may eventually replace sites like Getty Images and Shutterstock.

Even more newsworthy has been ChatGPT and other similar programs. Their ability to scour the internet in response to the user’s request and create natural language output is nothing less than uncanny. It can even tailor its results to match specific writing styles, from as broad as poetry or prose to as far as mimicking specific people’s writing styles.

Disruption is a grossly overused buzzword in tech, but if DALL-E and ChatGPT aren’t harbingers of disruption across multiple fields of human activity, nothing is. The precise nature of the changes that AI will bring can be much harder to predict.

The past decade has seen an explosion of startups that, though enormously diverse, fall under the new category of insurtech. Many of them have attempted to leverage artificial intelligence and/or machine learning to streamline one aspect of the world of insurance or another. The goal is typically some combination of reduced costs and increased speed, efficiency or personalisation.

One of the larger and better-known insurtechs promised to use machine learning to re-define an old, fragmented industry and focus on prevention and smarter underwriting. The results of these efforts led to a loss ratio of 161 per cent. The company was forced to reduce the number of products it offered and change its distribution models by focusing on indirect ones. Taking these steps succeeded in improving their loss ratio to merely 110 per cent, which remains unsustainable.

Another insurtech trailblazer promised transformational motor insurance pricing using machine learning to price drivers based entirely on behavioral attributes. Technology such as telematics would result in month-to-month pricing variability, yet the traditional attributes of consumer pricing still are largely used.

As a final case study, perhaps the best-known of all the insurtechs promised to reduce claims ratios and costs through better analytics and claim automation. However, despite achieving good claims resolution times, both their claims ratios and their operating ratios remain stubbornly in the same range as the traditional insurers with whom the company now partners.

What do all three above cases have in common? A growing use of technology as a basis for generating additional insights, or better processes – but a new insurance company that is still exposed to all the same macro conditions (specifically inflation, natural catastrophes and customer acquisition) as all the largest established players that are focused on direct, agent and digital distribution.

Trying to shoehorn artificial intelligence into insurance has not been an unqualified success. Does that mean that the project is doomed?

Quite the opposite.

The recent failures are merely pointing us in the right direction, which is away from hype and towards a steady development of substantial, sustainable and useful capabilities.

The reality is that artificial intelligence will penetrate every single aspect of the insurance industry to offer better products and services to the customers, while improving the bottom line for the shareholders. But these changes will not happen overnight and cannot be forced. Full transformation will take closer to a decade than a year, and we need to let the process play out as capabilities continually improve.

Some changes will happen sooner than others. Parametric insurance is one example. Flight delays are a live, real-world example, where no claim even needs to be filed; rather, the system automatically informs the traveler of both the delay and the compensation.

A little farther down the line, similar tools will be developed that expand on established use-cases with more complexity, such as home insurance. Imagine being informed by your insurer that it has detected a water leak in your home, assessed the floor damage, called for the relevant repairs, and issued an immediate prepayment to get sorted out.

Eventually, the total interconnectedness of all our devices and activities will reach the stage where all we need to do is ask Siri or Hey Google out loud, “What insurance should I get?” And that’s when we will have achieved total AI insurance transformation.

Of course, we can’t forecast where this will lead. One important unanswered question is the extent that legislation and regulation will play in determining AI’s role in the industry. In the meantime, we can prepare the data foundation that is the prerequisite for any robust AI system. Doing that requires determining, defining and activating as many data sources as possible, both within and without the insurer’s databases. This will include learning how to add market-level (public) data at scale to make the models and machine queries ever better.

Even as AI technology continues to develop, there is much for us to do so we can prepare for the future and meet it head-on.

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