AI could make use of India’s health stack data to expand the country’s health insurance coverage

Subscribe to enjoy similar stories.Nearly 70% of Indians lack meaningful health insurance. Our penetration rate— just 0.35% of GDP—is lower than that of China, Hong Kong and Taiwan. The conventional explanation is that Indians either do not understand insurance or cannot afford it.

But in reality, the problem is not behavioural as much as the fact that Indian insurers, for the most part, lack the population-level health data that they need to price risk accurately.When premiums are too high relative to actual risk, healthy people—who know they are unlikely to need expensive care—see no reason why they should buy coverage. When that happens, the risk pool gradually shrinks until it consists largely of those who expect to make claims. This, in turn, results in higher premiums, which, in turn, results in even more healthy people staying away, pushing our health insurance market into a vicious cycle that is hard to escape.

Take the example of diabetes, the single largest driver of chronic disease claims in the country. Today, insurance premiums for the disease are determined based on crude national averages or the prevalence of the disease across given age bands. This is despite the fact that its prevalence is known to vary widely across regions—from 4.8% in Uttar Pradesh to 26.4% in Goa.

None of this regional divergence is reflected in the premiums people have to pay.At its core, an effective insurance system is a mechanism for pooling risk. Individual premiums are not set based on what the insured person’s bills will cost, but on the expected average expenses of people like them. The larger and more diverse the pool, the more accurately risk can be priced, and the more stable premiums become over time.