It is difficult to open an insurance industry newsletter these days without seeing some reference to machine learning or its cousin artificial intelligence and how they will revolutionize the industry. Yet according to Willis Towers Watson’s recently released 2019/2020 P&C Insurance Advanced Analytics Survey results, fewer companies have adopted machine learning and artificial intelligence than had planned to do so just two years ago (see the accompanying graphic).

Executive Summary

P/C carrier executives have higher expectations about adopting machine learning techniques in 2021 than they had in 2019, according to a recent Willis Towers Watson survey. But their expectations were pretty high in 2019, too, and actual use is far short of predictions. Here, consultants from Willis Towers Watson explain some of the reasons, using pricing applications as an example and noting that difficulties in explaining results to customers and regulators as well as table-based legacy rating engines are obstacles to quicker adoption.

In the context of insurance, we’re not talking about self-driving cars (though these may have important implications for insurance) or chess-playing computers. We’re talking about predicting the outcome of comparatively simple future events: Who will buy what product, which clients are more likely to have what kind of claim, which claim will become complex according to some definition.

The better insurers can estimate the outcomes of these future events, the better they can plan for them and achieve more positive results. The accuracy of their estimates relative to their competitors is of particular importance, for example, allowing them to price more accurately, attracting better and avoiding worse risks. Analytics have applications across the insurance value chain, from marketing, client acquisition and retention to underwriting, pricing and claims management, as insurers look to squeeze more signal out of their data.

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