Data-rich insurance models could fail beyond a point

Policies that go too closely by individual risk profiles could end up killing the very idea of risk-pooling

- RAHUL MATTHAN

Insurance is a human innovation that ensures that in moments of crisis, no one individual has to bear the weight of misfortune alone. Pooling risk has allowed us to contract, innovate and prosper, but with the ubiquitous availability of granular data, that assurance is starting to be replaced by something colder and more precise.

The modern insurance industry dates back to a time when merchants gathered in coffee houses (such as Lloyd's) to collectively insure their vessels against sea-faring risks. Since then, it has expanded into other forms—health, property and life insurance—while remaining true to the fundamental economic principle of risk pooling.

The risks that individuals face can be quantified and priced. What cannot be foreseen is when misfortune will occur and at what scale. Individuals who worry about the downside of certain risks can join together to share them so that they can support those among them who actually end up suffering harm. When this pooling of risk is aggregated across a wide enough population base, insurance companies are able to forecast the frequency and severity of adverse events with enough accuracy to be able to set premiums at levels that are sustainable for all participants. This is the economic basis of the modern insurance industry.