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How Insurers Can Tip the Scales With AI

Scaling AI Bertrand Waché

The insurance industry is one of the oldest in the modern world. And like all aged, stately things, it moves both gracefully and slowly. One of the consequences of the industry’s inertia has also become one of its biggest pain points: its reluctance to evolve out of the many legacy technologies upon which it continues to rely. 

Across the many domains of insurance, an over-reliance on slow, unsophisticated, and difficult-to-use technologies—from pricing tools to accounting software to data management programs—leads inexorably to a loss of time, resources, and money. This is especially damaging to insurers focusing on homeowners insurance, auto insurance, and life insurance, among others; they exist in crowded, competitive spaces where margins tend to be razor thin. And the picture is made more complicated by the emergence of new entrants in the field, who tend to be lighter on their feet and more keen to adopt novel technologies that will give them a leg up over the established players.

But however painful and difficult it might seem to get out from under the grip of sclerotic technologies, the benefits of doing so far outweigh the consequences. In fact, as we’ll discuss in this blog post, research has both shown this to be true and put a value on it.

The 20/60/20 Problem

The insurance industry, like any other, is characterized by a varied distribution of success among its players. But as a recent McKinsey report makes clear, this distribution takes on a peculiar shape in the case of insurers. If we think of the field of insurers’ individual profits as falling along a scale separated into five quintiles, we begin to notice a striking pattern: the top twenty percent of insurers — the heaviest hitters — have raked in an average economic profit of $764 million over the last five years, whereas the bottom twenty percent posted an average loss of $976 million over the same period. 

But what about the middle? Strikingly, when we look at the majority of insurers, the curve goes flat. The middle 60% of them made, on average, $26 million in profit over the last five years, an uncomfortably thin margin that represents just how much instability lies in the broad middle of the pack. This narrow margin, though, presents a unique opportunity. It doesn’t take much to predict that the high-earning, top 20% of insurers will continue to generate profits and dominate the power curve. And it would be similarly safe to bet that most of those companies posting substantial losses in the bottom quintile won’t see their way out of the next couple of years. These truisms are themselves the simple products of inertia; once the ball has started rolling, it’s very hard to stop it. But the packed middle presents us with a different story.

You might say that the companies in the middle 60% haven’t gained any financial inertia in either direction; if the hill runs down to either side of them, they are teetering precariously at the peak. What they need, then, is a little nudge in the right direction — a little added weight to tip the scales in their favor. And this is where we come back to technology.

AI: The Snowball of Success

One thing the McKinsey report makes clear is that “moving up the power curve requires a laser focus on the factors that have an outsized impact on success, measured as economic profit.” The companies in the broad middle of the industry find themselves on a level playing field, and the ones that get ahead will be the ones that find a way to do something the others aren’t — something bold and impactful. 

Among the five “bold moves” that McKinsey notes as being successful, one of them sticks out: “Make game-changing function improvements in productivity.” At Dataiku, we see our mission as providing precisely this kind of function improvement in a bid to increase cross-team collaboration, drive efficiency, and ultimately increase profits. 

In fact, as another industry study demonstrates, developing an AI strategy is one of the most significant steps toward such improvements that an insurer can make. BCG’s report on AI in the insurance industry is appropriately blunt: ignoring AI is “risky business” for insurance CEOs. Many of these companies feel themselves stuck when it comes to AI for the simple reason that they lack the repositories of data that drive the impact of AI: “Insurers have infrequent interactions with their customers and thus have limited data on which to base their underwriting, pricing, and claims decisions,” the report states. 

But no company is helpless in moving from one stage of AI maturity to another. Dataiku specializes in helping clients across multiple industries develop their maturity strategically — all while reducing costs and driving value. All it takes to get there is that first bold step. And it’s a worthwhile one: BCG found that “insurers that invest significantly in AI are seeing the benefits, such as increasing net new business by 20% to 25% and reducing loss ratios by 2 to 3 percentage points.” 

For companies in the middle 60%, then, embracing an AI-driven data strategy could be their ticket out into the upper quintile. It is the nudge down the right side of the hill, the start of a movement that will only gain momentum with time — like a snowball growing in size — as the 20-25% figure from the BCG report makes plain. And while making such a decision may take time, it’s important to realize that there is not a moment to lose. The sooner one begins moving along the maturity curve, the sooner one can turn the idea of AI into an everyday corporate practice.

Grace and Speed Are More Compatible Than You Think

Among the competition that any legacy insurer faces is the average new entrant. Younger insurers are typically lean, tech-friendly, and light on their feet. This is especially true of so-called insurtechs, whose primary aim is to “transform segments of the insurance value chain by leveraging the latest technologies,” as BCG puts it. By embracing AI, these companies are attempting to get ahead of the curve and could turn themselves into major players in short order.

But what they don’t tend to have are the assets only precipitated by time: customer loyalty, brand names, deep rosters, profound ties to partners, and institutional knowledge of the industry. Leveraged correctly, these and other assets can be seamlessly integrated into a legacy insurer’s AI strategy, which includes expanding data-gathering capabilities and building smart predictive models. Over time, and with increasing maturity, AI-powered companies may just find their way out of the middle of the pack and into the vanguard.

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