India’s health insurance market is caught in a vicious cycle: healthy people won’t buy coverage because premiums are too high, and premiums are too high because healthy people won’t buy coverage. If insurers can use granular health data to price risk more accurately, coverage may once again become worth buying.

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.

Mispriced Risk

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. This is why the vicious cycle described above is so damaging: healthy people who exit the pool don’t just reduce revenue, they make the pool itself less representative, the pricing less accurate, and the premiums higher.

Break the Cycle

Better data could break this vicious cycle. If insurers move beyond crude age-band or national-average assumptions and price risk at genuinely granular levels—by region, occupation, co-morbidity profiles and lifestyle markers—they can offer premiums that healthy cohorts might actually find worth paying. A 32-year-old software professional in Bengaluru with no family history of chronic illness should not be priced as though she carries the average risk of all Indians aged between 30 and 40. She almost certainly doesn’t. But insurers need the data to be able to take that call.

For this to happen, we need population-level health data that is sufficiently granular to be useful. Accurate actuarial models built on rich, longitudinal data don’t just lower premiums for healthy people, they improve the efficacy of the entire system. Lower premiums attract broader participation. Broader participation generates better data. Better data results in finer segmentation. Finer segmentation allows even more accurate pricing, effectively inverting the vicious cycle.

Where can we source this data from? As it happens, India has a digital infrastructure capable of processing vast amounts of health data at scale. The Ayushman Bharat Digital Mission (ABDM) currently has access to over 500 million health records across 80,000-plus healthcare facilities; while its primary objective is to provide access to patient records across the healthcare system, it should not be hard to modify it so we can use it to improve our actuarial intelligence. The key is doing so safely, without putting the personal data of patients at risk of exposure in the process.

The original ABDM blueprint had an anonymiser module that was designed to de-identify health records for use in privacy-preserving applications. To the best of my knowledge, this has not yet been built, but it is evident how an effective anonymisation solution could make ABDM data useful for this purpose. Since the Digital Personal Data Protection Act of 2023 only applies to “personal data,” properly anonymized data would not fall within its ambit. Which means that it could be used to build actuarial models without requiring the consent of the individuals concerned.

Modeling Risk

The last element that needs to be added to the mix is artificial intelligence (AI). Traditional actuarial methods rely on structured tables and known risk factors—age, pre-existing conditions and occupation. They work well enough when the categories are broad and the data is limited. But if we use AI, applying it to the large anonymised datasets that ABDM can make available, we should additionally be able to identify non-obvious correlations between health indicators, allowing us to identify emerging disease clusters before they appear in claims data. This will enable us to model risk trajectories across demographic cohorts that conventional methods would miss entirely.

At the end of the day, insurance is not really about policies or premiums. It is about information. When insurers cannot see risk clearly, they will price policies defensively, and that is when the system begins to unravel. The availability of useful data allows risk to be measured more precisely, making it possible for insurance markets to expand and stabilise.

India needs to unlock the actuarial intelligence embedded within the data that its digital health infrastructure is beginning to generate. Once we do that, we may discover that the biggest barrier to universal insurance coverage in the country was never affordability, but visibility.