[This article is built around thought I shared in the round table titled “Insuretech for India” at Stride Forward 26]
India built the world's most complete public digital
infrastructure to include people. The next test is whether it uses AI to
protect more of them, or to price the
riskiest ones out.
Insurance is the one financial product designed to work by pooling
strangers together. The healthy subsidise the sick, the lucky subsidise the
unlucky, and everyone buys protection against a future none of them can
predict. That is not a flaw in the model. It is the model. Artificial
intelligence is now very good at predicting exactly who will get sick, who will
crash, and whose house will flood, and that ability, left to run on its own
logic, quietly dismantles the thing that made insurance worth having.
This is the real question hanging over the insurance industry, and it is
sharpest in India, not because India is behind, but because India is unusually
well equipped to take it either way. Over the last decade the country has
assembled the most complete public digital infrastructure in the world: a
billion-scale digital identity, real-time payments, consent-based data sharing,
digital documents and signatures. That stack was built, deliberately, to
include people who markets had left out. The same stack, pointed at insurance
and combined with AI, can be used to include far more people, or to segment them so finely that the ones who
most need cover can no longer afford it. India will have to choose. Most
countries will not get to make that choice as consciously, because they lack
the rails to make either outcome happen at scale.
So it is worth being clear about what is actually at stake, and where the
genuinely hard problem lies — because it is not where most of the industry
conversation puts it.
The easy part: insurance is about to disappear into everything else
Start with the parts that are, by now, close to consensus. The first wave
of insurance technology everywhere, cheaper distribution, faster underwriting,
quicker claims, lower operating cost, is
largely a solved direction of travel. Three shifts follow from it, and they
will define the next several years.
Insurance becomes embedded rather than sold. It stops being an
annual contract you remember to renew and becomes a service that attaches
itself, invisibly, to something else you are already doing. You buy a
two-wheeler and accident cover comes with it. You take a home loan and property
cover is part of the transaction. You book a trip, finance an MSME invoice, buy
farm equipment, or see a doctor on a health platform, and the relevant
protection is simply present. India's digital platforms make this possible at a
scale few markets can match. The right ambition is for insurance to become, in
the phrase I keep coming back to, always present but almost invisible.
AI settles the straightforward claims in near real time. A large
share of claims are simple, honest, and slow only because a human has to look
at them. Those will be assessed and paid in minutes. This matters less as an
efficiency story than as a trust story, which I will come to.
Every citizen carries a portable insurance profile. Identity,
verified financial history, health records shared with consent, property
records, and past claims can travel with the individual rather than being
locked inside one insurer. That lets a person move between insurers without
starting from zero each time, and it lets underwriting happen in real time at a
fraction of today's cost.
None of this is the hard part. It is expensive and fiddly to build, but
the direction is not in doubt and the benefits are real. If this were the whole
story, the correct posture would be enthusiasm and patience.
The barrier that technology alone does not fix
The deeper obstacle in India has never been mainly technological. It is
trust, and it has four distinct faces. People do not reliably know what to buy,
whether a claim will actually be honoured when it matters, whether the premium
they are quoted is fair, or whether the whole process is simple enough to be
worth attempting. Every one of those is a reason someone who should be insured
is not.
Technology helps with each, AI-assisted advice for the first, transparent
pricing for the third, paperless onboarding and cashless claims for the fourth.
But the second, whether claims are honoured, is where technology and trust
actually meet. A claims process that pays honest claims in minutes, visibly and
repeatedly, builds the kind of trust that a marketing campaign cannot. That is
why real-time claims settlement matters more than its efficiency suggests: it
is the mechanism by which an industry with a credibility problem earns credibility
back.
Why India can build rails, not just products
Here is where India's position differs from most markets, and it is worth
stating precisely rather than triumphantly. Elsewhere, the natural unit of
progress is the company: a better insurer, a smarter underwriting model, a
slicker app, each building its own private ecosystem. India has the option to
build the shared layer underneath all of them, common digital rails for insurance in the way
real-time payments became common rails for money, so that insurers compete on
top of shared infrastructure instead of each rebuilding the plumbing.
The reason India can attempt this is that most of the foundation already
exists and is public: verifiable identity, a consent architecture for sharing
financial and health data, digital documents and signatures. Very few countries
have that combination in public hands. The missing layer is insurance itself , the
standards and rails that would let a verified individual be underwritten,
insured, and served across providers with their consent and without friction.
Get that layer right and the cost of issuing and servicing a policy falls far
enough that protecting a low-income family becomes commercially viable rather
than charitable. This is the genuinely globally significant experiment, and it
is why people outside India should be watching it: it is a test of whether
insurance can be run as public infrastructure rather than only as a private
product.
But this is exactly where the capability turns double-edged, because the
same rails that can underwrite a poor family in real time can also price that
family out in real time. The infrastructure is neutral. The choice is not.
The tension at the centre: pooling versus prediction
Traditional insurance rests on risk pooling. AI, fed with rich
personal data, pushes relentlessly toward risk segmentation, pricing each individual according to their own
predicted risk. Taken to their logical ends, these two ideas are in direct
conflict. The better AI becomes at predicting risk, the worse insurance becomes
at sharing it.
Follow the logic to its conclusion. Healthy, low-risk people pay very
little, as they should on pure actuarial grounds. High-risk people face
premiums that climb until cover is effectively out of reach. The result is
quietly perverse: the people who most need protection are the ones priced out
of it. That does not just produce an unfair market. It hollows out the social
purpose of insurance altogether, because a pool that has expelled everyone
likely to claim is no longer performing the one function that justified it.
This is not a hypothetical that regulators have failed to notice. It is
precisely why many jurisdictions already prohibit insurers from using certain
information, genetic test results, disability status, pregnancy, some
pre-existing conditions, various protected characteristics. Those prohibitions
are not technological limits; the data is often perfectly usable. They are
deliberate policy choices to preserve solidarity even when better prediction is
available. AI does not create this dilemma. It sharpens it to a point, by
making near-perfect prediction cheap and universal rather than partial and
expensive.
The better question: predict risk, or prevent it?
There is a way out of the trap, and it comes from asking a different
question. What if AI were used not to charge sick people more, but to make them
less likely to be sick?
Imagine an insurer that continuously observes, with consent, the early
signals, rising blood sugar, worsening
blood pressure, an irregular heart rhythm. The segmentation instinct is to
reprice the moment the risk appears. The alternative is to intervene: a
teleconsultation, nutrition coaching, subsidised medication, a fitness
programme, an early screening. If the intervention works, the person stays
healthier, hospitalisations fall, claims drop, the insurer's costs fall with
them, and society carries a lighter burden of disease. The insurer stops being
a payer of claims and becomes a manager of health outcomes. Prediction is put
to work preventing the loss rather than pricing it.
That reframes the whole debate. The question regulators and builders
should be asking now, at the start of AI adoption and not after the practices
have set, is a simple one with large consequences: should AI be used
primarily to predict risk more accurately, or to reduce risk before it
materialises? An industry that mostly predicts becomes more exclusionary
with every improvement in its models. An industry that mostly prevents becomes
more inclusive as its models improve. Same technology; opposite social result.
The difference is a design choice, and design choices are easiest to make
early.
A question for society, not for the algorithm
Insurance has always balanced two principles that pull against each
other: actuarial fairness, which says each person should pay according to their
own risk, and social solidarity, which says a pool should absorb risk on behalf
of those who draw the unlucky number. For most of the industry's history the
balance was set by ignorance, insurers
simply could not price individuals finely enough to fully abandon the pool. AI
removes that ignorance. It will make actuarial fairness very nearly perfect.
Which means the balance can no longer be left to accident. The real
question is no longer what insurers are able to price, but how much
solidarity a society chooses to keep once perfect pricing is possible. That is
not a question an algorithm can answer. It is a question for society, and it
has to be answered on purpose.
India is unusually well placed to lead that conversation, and not by
coincidence. Its digital public infrastructure gives it prediction capabilities
that few countries can match, and its public policy has, through that same
infrastructure, consistently chosen to expand inclusion rather than optimise
markets for their own sake. That combination is rare: the technical capacity to
segment perfectly, paired with a stated preference for including everyone. The
test for Indian insurance technology — and the reason the rest of the world has
a stake in how it goes — is whether it uses AI to expand protection or
merely to refine pricing.
India's last financial inclusion story was about opening bank accounts,
and it largely succeeded. The next one will not be about accounts at all. It
will be about how many lives we choose to protect once we finally have the
tools to protect, or to exclude, every one of them.
The better a machine gets at predicting who will suffer, the more deliberately a society must decide to stand with them anyway.
