Library: BCG – Ignoring AI Is Risky Business for Insurance CEOs

Executive summary:

Insurers need to be AI ready 

AI is transforming business and society with algorithms that improve operations, enable the metaverse, and alter the ways companies interact with individual consumers. This transformation places insurance companies at a crossroads: they can either make a serious commitment to AI—or ignore this impending change and risk losing the ability to compete with their peers.

The time for AI is now

An insurer’s ability to optimise sales, distribution, pricing, and claims management will be vastly improved if it can use AI to collect, analyse, and embed data-driven insights into its core processes. Still, it gives rise to a conundrum: AI can transform the insurance industry, but, to do so, it needs data —which is exactly what insurers often lack.

The imperative for insurance CEOs is clear: it’s time to build AI-ready organisations.

The Changing Nature of Data

Insurers have infrequent interactions with their customers and thus have limited data on which to base their underwriting, pricing, and claims decisions.

More AI-orientated industry players, such as insurtechs, have got around this by putting digital and data at the core of their business models. These startups, run by digital natives, have had early success in some markets, such as auto and home insurance. The amount of money flowing their way suggests that they have the confidence of investors, who clearly believe in AI’s ability to disrupt the industry and capture a fair share of its profit pools.

Change is essential

Insurers hail from a tradition steeped in data of a different sort: historical and static data that comes from claims databases and underwriting questionnaires. But insurers can and must change.

First, they can begin to access larger volumes of customer data by positioning themselves as members of a consumer-information ecosystem.

They can form partnerships with customer-facing entities—such as home-inspection companies, smart-home-device manufactures, OEMs, travel agencies, and other organizations—and work with them to build more comprehensive customer data profiles.

Where outright data sharing is not possible because of privacy laws, approaches such as federated learning can assist by effectively enriching AI models without an actual exchange of data.

Second, insurers can access more customer data by developing new ways to increase their interactions with existing customers, by using rate-pricing guides or calculators on their apps and websites, for example.

Both approaches can provide insurers with the information they need to effectively implement AI. Some insurers have already started down this path and are seeing positive results.

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