The shift from traditional systems to Digital India raises concerns about public privacy and security, as greater reliance on digital systems can create new opportunities for fraud and scams. Aadhaar is one pillar of what the government now calls India’s Digital Public Infrastructure and how AI could change that DPI for making it faster and more reliable is the biggest evolution.
Every month, close to 20 million people across India get turned away by a fingerprint scanner or a face-authentication camera that’s supposed to hand them a subsidised ration bag, a pension, or a bank withdrawal. That failure rate — a little over 6% of roughly 312 million Aadhaar-based authentications attempted monthly has stayed almost unchanged for a decade, according to UIDAI’s own parliamentary disclosures.
It’s a strange fact to sit with, because DPI includes the stack of identity, payments, and data-sharing rails — Aadhaar, UPI, DigiLocker, Account Aggregator, ONDC — that more than a billion Indians touch, often without realising it. AI is now being wired into nearly every one of those rails. Some of it is already live and working.
What Is India’s Digital Public Infrastructure
Aadhaar is the identity layer — a biometric ID held by nearly every adult in the country. UPI is the payments layer, the real-time system that lets any bank app talk to any other. DigiLocker and Account Aggregator are the document and data-sharing layers, letting people share verified paperwork or consented financial records instead of photocopies.
ONDC is the newest piece, an attempt to do for e-commerce what UPI did for payments: an open network any app can plug into, rather than one company owning the marketplace.
Each of these was built as plumbing — deliberately boring, interoperable infrastructure. AI is the first layer being added on top that isn’t boring at all, and it’s arriving fastest in fraud detection, credit, language, and now shopping.
Fraud Detection at a Scale No Human Team Can Watch
UPI now clears more than 750 million transactions a day, a number NPCI expects to keep climbing toward a billion. At that volume, catching fraud after the money has moved is close to useless — NPCI’s fraud-detection platform processes roughly 2 terabytes of transaction data daily and needs to score risk within about 20 milliseconds, fast enough to block a payment before it settles.
Its MuleHunter AI tool has already flagged more than 4.7 lakh mule accounts — the disposable, borrowed accounts fraud rings use to bounce stolen money through four to six hops before cashing out.
NPCI is now piloting what it calls a “federated model” with three or four banks: instead of pooling everyone’s raw transaction data in one place, banks and NPCI compare AI-generated risk scores on a customer, getting the benefit of shared pattern-matching without centralising anyone’s actual financial history.
NPCI’s leadership has said openly that the next leg of UPI’s growth depends on AI doing three jobs at once — catching fraud, extending credit based on payment history, and onboarding the next half-billion users through voice and regional-language interfaces instead of English-only screens.
Half of India Still Can’t Access Bank Credit
For decades, an MSME owner without three years of clean, bankable paperwork was mostly invisible to formal lenders — not un-creditworthy, just un-scoreable.
The Account Aggregator framework, built on the RBI’s 2016 Master Direction and NITI Aayog’s Data Empowerment and Protection Architecture, changed the plumbing: with a customer’s explicit consent, a lender can now pull real bank statements, GST filings, and insurance records through a single encrypted pipe instead of chasing PDFs.
As of the framework’s fourth anniversary in 2025, 112 financial institutions had gone live on both sides of that pipe, with hundreds more connected as data providers or users. AI models sit on top of that data flow, and lenders report the payoff is real: industry estimates put improvements in default-prediction accuracy at 15 to 25%, and processing times that used to run one to three weeks are increasingly settled same-day.
This isn’t unregulated black-box lending, though — RBI’s digital lending rules require a lender to explain, in terms a risk officer or auditor can actually interrogate, why a model approved or rejected an application, which puts a real ceiling on how opaque these systems are allowed to get.
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No More Language Barrier: Treating Language Like Infrastructure
India has more than one language and an AI layer that drops the barrier between languages is Bhashini. In India, MeitY’s National Language Translation Mission became fully AI-powered translation engines and it now sits underneath every government services app from January 2026. Portals like DigiLocker, UMANG, e-Shram running into 22 Indian languages.
A Bhashini-powered voice assistant called Kumbh Sahaiyak used a Llama-based model to field pilgrim questions in 11 languages in real time — arguably the clearest proof yet that a DPI project can meet people in their own language at genuine crowd scale, not just in a pilot deck. If India’s Digital Public Infrastructure’s whole promise was ever about reaching people who can’t fill out an English form, Bhashini is the piece actually doing that, one API call at a time.
The Part AI Might Not Fix: Aadhaar’s Exclusion Problem
The government’s current bet is that AI-driven face authentication — rather than fingerprint or iris scanning, which struggle with a manual labourer’s worn fingerprints or an elderly person’s cataracts — will close that 6.5% failure gap.
It’s already been tested at real scale with UIDAI ran a face-authentication proof of concept at NEET-UG 2025 exam centres with the NTA and NIC, and by 2026 similar AI-camera verification, backed by liveness checks and a fingerprint or manual fallback, was standard at UPSC exam halls. Officials describe the results as promising.
Researchers who’ve actually gone door to door are less convinced: a survey across 32 villages in Jharkhand by economist Jean Drèze and co-authors found exclusion errors running as high as 20% in areas where biometric authentication was required for every ration purchase — and critics note that swapping fingerprints for face scans doesn’t remove the underlying design flaw, which is that a single biometric check, controlled by a ration dealer or bank teller rather than the beneficiary, decides whether someone eats that month.
There’s a second, quieter concern sitting alongside is the more AI systems sit on top of Aadhaar-linked data — UPI payments, SIM registrations, app logins — the easier it becomes to assemble a detailed behavioural profile of someone, usually described publicly as fraud prevention rather than surveillance. Both things can be true at once.
AI-based face authentication may genuinely cut into the 20-million-a-month failure count, and the same infrastructure may quietly expand what the state and private platforms can see about a person’s life. That tension, not a clean technical fix, is the actual story here.
Agents That Could Soon Shop and Transact for You
The newest layer is barely a layer yet. ONDC, India’s government-backed alternative to Amazon and Flipkart, runs on the open-source Beckn protocol, which had onboarded more than 900 network participants across grocery, food delivery, mobility, and financial services by 2026.
FIDE, the nonprofit behind Beckn, has already demonstrated BecknGPT — an experimental agent that lets a ChatGPT-style assistant browse and complete an ONDC purchase on a shopper’s behalf and mainstream apps including Swiggy, BigBasket, and Flipkart have started exposing their catalogues directly to AI shopping assistants.
It’s genuinely early, more demo than daily habit right now. But the direction matters: if UPI proved India could build a public payments rail that private apps compete on top of, ONDC plus AI agents is the government’s bet that the same model can work for commerce itself, without any single platform owning discovery.
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Why Other Countries Are Copying India’s Homework
Whatever India’s Digital Public Infrastructure domestic wrinkles, India has turned it into one of its more effective diplomatic exports.
During its 2023 G20 presidency, India pushed Digital Public Infrastructure as a global development model, the framework receiving endorsement from 20 member nations. By 2026, the cooperation agreements between India and other 23—24 countries have been signed for helping them build their own version of Aadhaar, UPI, or DigiLocker by sharing open-source blueprints rather than selling a finished product.
UPI itself is already live in eight-plus countries, from the UAE and Singapore to France and Mauritius, and Kenya is reportedly building a national digital-identity stack directly modelled on India’s approach.
Analysts who track this call it the “Aadhaar Paradox”: India is exporting a model with real, unresolved problems at home — the exclusion numbers above, heavy market concentration in UPI apps, ongoing data-protection debates — alongside genuine, population-scale successes that few other countries have managed.
Both halves of that sentence are true, and any AI layer India bolts onto its own DPI will likely travel abroad along with everything else.
What Determines Changing India’s Digital Public Infrastructure With AI Goes Well
A few concrete things to watch over the next year, rather than general optimism about AI transforming governance.
First, whether NPCI’s 30% market-share cap for individual UPI apps — currently set to take effect December 31, 2026 — actually holds this time, since two apps still control more than 80% of transaction volume and AI-driven credit and voice features could widen that gap further or finally give smaller players something to compete on.
Second, whether UIDAI’s AI-based face authentication measurably moves the 6.5% Aadhaar failure rate in its next couple of annual disclosures, or whether as analyzer expect — it just changes which biometric ends up failing people.
None of these are settled yet. Both will tell you, faster than any government press release, whether AI made India’s Digital Public Infrastructure more inclusive, or just faster at doing the same thing to the same people.
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Conclusion
AI could make India’s Digital Public Infrastructure faster, more accessible, and better at handling problems that humans simply cannot manage at this scale. From spotting fraud across UPI transactions to helping lenders understand small businesses and breaking language barriers, the benefits are already becoming visible.
But the bigger question isn’t whether AI can make India’s Digital Public Infrastructure more efficient. It’s whether it can make it more inclusive without creating new problems along the way. Aadhaar’s authentication failures, concerns around data profiling, and the risk of AI-powered systems becoming harder to challenge show why speed alone isn’t enough.
India has already built the digital rails. The next phase is about deciding how intelligently, transparently, and fairly AI runs on top of them. The success of that shift will depend not just on better models, but on whether the people using these systems actually have fewer barriers when they need them most.
