In a public hospital, the hardest part is often not the treatment itself, but the administrative hurdles around it in the form of paperwork, eligibility checks, approvals, delays, and repeated visits. For citizens, this is what the state feels like in practice: not policy intent, but the ability to deliver services reliably and correct errors quickly. AI can help administrations spot bottlenecks early, route grievances faster, and reduce avoidable delays so delivery improves without reducing accountability. This is not about automating entitlements; it is about strengthening the administrative systems around them. This is why the AI debate in India needs to be anchored in state capacity, not just innovation.
Union Budget 2026–27 is an opportunity to make that shift concrete. India has already signalled intent through the IndiaAI Mission, approved by the Union Cabinet with an outlay of ₹10,372 crore over five years, spanning shared compute, datasets, skilling, applications, and safe and trusted AI. The Budget will now be judged on whether it strengthens this mission in ways that improve real public services and whether it funds the guardrails that prevent predictable harms.
First, watch for continuity in IndiaAI funding and if it is designed for sustained use.
AI systems require reliable shared computing capacity and secure environments for public applications. The clearest signal will be whether allocations support this as an ongoing public capability, not a one-time asset so public-interest deployments can move beyond pilots without duplicating tools across departments. Experience from World Bank supported open digital networks (ONS-style) suggests that shared rails and good data systems matter more than one-off pilots.
Second, watch whether AI is being funded as governance improvement. IndiaAI’s impact will depend on adoption inside large programmes, where administrative delays and grievances accumulate. The Budget can signal seriousness by creating a modest, explicit window for programme-linked deployments and independent evaluation, tying spending to measurable administrative outcomes rather than fragmented demonstrations. A serious AI push in government is ultimately a people-capacity push. Budget 2026–27 will be judged on whether it finances the human capability to deploy and govern these systems, programme managers, data stewards, and evaluation capacity rather than assuming technology alone will improve administrative performance.
Third, watch how IndiaAI will reach the states. Since many flagship welfare programmes run through state departments and district administrations, uneven adoption will translate into uneven outcomes. A key budget expectation is therefore targeted support for state readiness not through state-wise AI handouts, but through funded enablers such as training roles, data stewardship capacity, and procurement standards that embed transparency and auditability. Since state budgets follow soon after the Union Budget, the Centre’s choices on IndiaAI can act as a template for states encouraging them to invest not just in pilots, but in the people, data systems, and evaluation capacity needed to deploy AI responsibly in public programmes.
The Budget’s AI story should be read through outcomes. The strongest signal will be whether IndiaAI funding builds shared capability that departments can actually use, whether adoption is tied to measurable improvements in programme performance, and whether states are equipped to deploy AI transparently and safely. With India’s strong foundations on digital public infrastructure, AI can strengthen public services and expand inclusion but only if guardrails are financed early, from independent evaluation and audits to grievance redress, and human oversight.
If this is done right, IndiaAI can become a durable investment in state capacity, not just another technology push.
This was first published in “Express computer” on 25th January 2026.
The transition from the draft “Microcredit Bank” to the final Microfinance Bank Ordinance of 2026 represents a significant hardening of the regulatory floor. While the previous draft felt like an experimental foray into parallel banking, the final ordinance anchors these new entities firmly within the central banking system, albeit with a rigid social cage that may redefine the very meaning of an “investor.”
This evolution marks a shift from a “regulatory island” to a bridge overseen by the Bangladesh Bank. By placing these entities under the prudential rigors of the Bank-Company Act, 1991 and the Bangladesh Bank Order, 1972, the state has effectively ended the era of microfinance as a separate, light-touch domain.
This regulatory tightening is accompanied by a steep escalation in financial requirements. The bar for entry has been raised significantly, with authorised capital now set at Tk 500 crore and minimum paid-up capital at Tk 200 crore—doubling the requirements of the original draft. Yet, the most radical pivot lies in how the law treats profit. In a move that prioritises borrower-centricity over market returns, general investors are capped at recovering only their initial investment, while borrower-shareholders are exempt from this limitation. This strategy is clearly designed to incentivise ownership among the poor rather than the private elite, but it creates what might be called the “uninvestable paradox.”
By doubling down on this “social business” mandate, the ordinance essentially shuts the door on traditional private capital. No rational entrepreneur will deploy risk capital into a Tk 200-crore venture where the potential upside is legally capped at zero. This creates a deliberate systemic push: by making the bank uninvestable for the general market, the law effectively forces these institutions toward a Grameen-style structure, where borrower-shareholders must become the true owners. Indeed, the law mandates that this group must hold at least 60 percent of the capital. However, this noble philosophy faces a historical and practical hurdle: the risk of ownership without power.
The precedent of Grameen Bank offers a sobering lesson in this regard. The bank’s governing “Sixteen Decisions” emerged not from a top-down mandate, but from intensive dialogues held by borrower-leaders in the early 1980s.
This grassroots codification of social policy—abolishing dowry and mandating child education—forced the bank to operate as a development agency. We see the fruits of this influence in the push for non-traditional products like housing loans, which saw Tk 280 crore disbursed for rural homes by 2022. Yet, researchers note a persistent “literacy chasm.” Because a vast majority of borrower-directors have historically lacked formal education, their influence often remains concentrated on social policy rather than financial auditing. While they “own” the bank, professional staff continue to operate the complex financial levers, leaving the owners to influence the soul of the institution but rarely its spreadsheets.
The 2026 Ordinance risks codifying this disparity. While it grants borrower-shareholders the right to elect four out of nine directors, it remains dangerously silent on mandated financial literacy. Without a rigorous framework to equip a rural borrower to oversee capital adequacy ratios or liquidity management, their 60 percent majority ownership risks becoming a legal fiction.
In the absence of true decision-imposing power, authority will inevitably consolidate in the hands of the managing director and nominee directors from the institutional side. The borrower-directors may find themselves reduced to “token” representatives—present for compliance, but silent during the complex maneuvers of fractional-reserve banking. Perhaps most concerning is the “capital squeeze” inherent in this model. If a liquidity crisis hits, the borrower-shareholders—the “true owners”—lack the personal wealth to provide emergency equity support.
By alienating general investors through the dividend cap, the bank loses its natural “lender of last resort” at the shareholder level, leaving it vulnerable to systemic shocks that the poor cannot buffer. Ultimately, the Microfinance Bank Ordinance is a bold attempt to institutionalize social equity, but it builds a boardroom where the majority owners are structurally positioned to be the least heard.
Unless forthcoming rules mandate aggressive governance training and simplified reporting, these banks will not be instruments of empowerment but sophisticated pieces of tokenistic theater.
This was first published in “The Daily Star” on 17th February 2026.
Like every week, Rakesh Sengar arrived at his neighborhood ration shop just after it opened. For years, he used his national ID to get staple foods from the same shop. Yet, the authentication failed this time. The operator retried, but an error code flashed on the device, which is one of thousands generated across the system each hour. Rakesh had to leave and was asked to return later.
At the national level, the identity platform reports strong performance. It records Rakesh’s failed attempt, but as one among millions of daily transactions, it disappears into averages that suggest everything is working as intended.
National ID systems have become the primary gateway to both public and private services worldwide. India’s Aadhaar system has processed more than 150 billion authentication transactions with nearly 550 entities, which span banking, telecom, and government services that rely on it daily. On average, more than 90 million authentications occur each day. The World Bank reports that digital identity enables individuals to securely authenticate themselves remotely, which is essential as services move online. The World Economic Forum describes national ID as part of the shared infrastructure that enables economic and social activity at scale. Yet, this centrality creates a vulnerability: when national ID authentication fails, citizens lose access to essential services.
Global attention centered on enrollment during the past decade. Enrollment is essential, but this is no longer sufficient. Today, exclusion most often occurs during authentication and use, not at enrollment. In a country that processes 100 million transactions monthly, an authentication system with 90% accuracy would still produce around 10 million false matches in a comprehensive search. These systems also erroneously deny essential services to potentially several million individuals.
Privacy International documents how national ID systems, designed for efficiency and fraud prevention, are often riddled with logistical failures that exclude vulnerable populations. The World Bank acknowledges that failures in biometric authentication mechanisms can lead to people being excluded from access to related services. Without transaction-level visibility, these failures remain invisible.
This blog proposes a novel transaction-level observability framework for national ID programs. This framework is a governance architecture that enables authorities to see, diagnose, and act on authentication failures at the point where inclusion or exclusion occurs. The transaction-level observability framework is more than a dashboard or a reporting tool. It serves as a governance capability enabled by data architecture, institutional workflows, and decision rights, which can be visualized as dashboards at an interface layer.
The framework: Eight layers of observability
Figure 1: Stages of the proposed transaction-level observability framework
We can understand the transaction-level observability framework as eight interlinked layers that run from the point of use to the point of accountability. At the base is the citizen experience, where real inclusion or exclusion unfolds. Whether a farmer seeks an agricultural subsidy, a patient accesses a health clinic, or a person buys food at a ration shop. Above this lies the core identity infrastructure, such as India’s Aadhaar Central Identities Data Repository (CIDR), with its massive daily transaction volumes and decentralized systems, or Estonia’s X-Road, which links hundreds of institutions.
These systems store transaction data that form raw observability signals, which are then analyzed to generate diagnostics and actionable insights. Visual decision interfaces translate patterns and trends for human understanding, while decision and escalation workflows connect insights to actions. At higher governance levels, institutional ownership determines who responds and how, while an overarching layer of risk, rights, and accountability ensures that differential impact is recognized and addressed rather than hidden behind averages, especially among vulnerable groups.
Each layer exists because a framework must address all dimensions of system success and failure. Citizen experience without verified data lacks accountability, data without analysis obscures root causes, analytics without workflows fail to change outcomes, and workflows without governance can be ignored or misused. Estonia’s digital ID ecosystem shows how decentralized exchanges and logged access contribute to public trust by enabling citizens to track who accesses their data. Conversely, large centralized systems, such as India’s Aadhaar, have revealed profound exclusion risks when authentication failures result in denied benefits or service delays, which highlights why rights and risk governance must cap any observability architecture.
For implementors, this framework is a lens for interpretation and action. It asks where failures occur, what patterns they reveal, who is responsible, and what corrective pathways exist. It also means to embed real escalation pathways that connect frontline feedback to policy review and institutional oversight. This framework is more than a roadmap to dashboards as it is a blueprint for people, processes, and policies that ensure observability drives equitable outcomes.
Dashboards as the interface layer
Visualizations appear at the interface layer, as a bridge between complex systems and human decision-makers. Dashboards can expose patterns, establish benchmarks at the national, regional, or institutional level, and help decision-makers move from symptoms to root causes. Below, we present an illustrative architecture to show how transaction-level observability can support data-driven interventions, drawn from our work with a national ID authority.
This overview representsonly the architecture of the visual layer. In the next blog, we explore detailed analytical views thatstart from the overviewto individual merchant performance and error analysis dashboards to enable drill-down diagnosis and evidence-based intervention. Together, these frameworks and architecture enable transaction-level observability to transform national ID governance from reactive oversight to proactive, accountable stewardship of digital identity infrastructure.
MicroSave Consulting (MSC) convened a high-level pre-summit event in New Delhi focused on practical pathways to scale climate-resilient agriculture using artificial intelligence (AI) anchored in Digital Public Infrastructure (DPI). The discussion brought together policymakers, development partners, technology leaders, and practitioners to move from fragmented pilots to sustained public delivery at scale.
Mitul Thapliyal, Managing Partner, MicroSave Consulting, framed climate impacts as a seasonal reality for farmers and noted that farmers often recognise changing conditions without using the vocabulary of climate change, creating a translation gap that can limit uptake of climate-focused solutions.
Hemendra Mathur, Venture Partner, Bharat Innovation Fund and Co-founder, ThinkAg, emphasised that Indian agriculture is data-rich and highly complex, and that AI will deliver scale only if systems can connect across registries with compliant data sharing and privacy safeguards. C. V. Madhukar, Chief Executive Officer, Co-Develop, cautioned that not every pilot merits scale, and that responsible adoption requires clear scale criteria, federated-first data governance that avoids both silos and risky centralisation, and trust safeguards across both technology and institutions.
Discussions on private-sector innovation highlighted that AI services must be built around farmer realities to be adopted and sustained. Neeraj Huddar, Product Manager and GTM Lead, Digital Public Infrastructure, Google India, emphasised that scalable AI needs shared digital rails with clear protocols, secure access, verifiable credentials, audit logs, and feedback and grievance pathways, with data quality and provenance as the most sensitive requirements. Nidhi Bhasin, Chief Executive Officer, Digital Green Trust, shared lessons from scaling voice-first farmer advisory, noting that adoption at scale depends on trust, localisation, and continuous feedback loops, especially for women farmers who face access barriers but sustain strong engagement once onboarded through trusted channels.
The conversation also focused on what it takes for states to move from digitisation to AI readiness. Kirti Pandey, Country Engagement Partner, Centre for Open Societal Systems (COSS), noted that AI readiness requires advisory and market information to be published in consistent, machine-readable formats aligned to shared protocols, so services can be integrated on common rails rather than rebuilt state by state. Jagadish Babu, Chief Operating Officer, EkStep Foundation, emphasised that institutions are the trust anchors in public systems, and that APIs are institutional commitments, requiring AI layers to fit government workflows and accountability.
Navin Bhushan, Partner, MicroSave Consulting, reflected on implementation lessons from Bihar Krishi, including the importance of unifying farmer-facing services, strengthening system reliability as schemes evolve, and building institutional ownership and review mechanisms that sustain delivery beyond a single pilot cycle.
In the closing session on India and the Global South, Srivalli Krishnan, Deputy Director, Agricultural Development (Asia), the Gates Foundation, emphasised AI’s potential to reduce information asymmetry for small and marginal farmers by enabling voice-based access, timelier advisories, and lower-cost service delivery. She also noted that reuse and replication depend on clear value for the next adopting state or institution and on breaking data silos across departments to enable integrated services.
Other participants included Siddharth Chaturvedi, Senior Program Officer, Agricultural Development (Asia), the Gates Foundation; Jatin Singh, Founder and Managing Director, Skymet Weather Services Pvt. Ltd.; Poorna Pushkala Chandrasekaran, Chief Executive Officer, Samunnati Foundation; Gauri Bandekar, Advisor, Royal Norwegian Embassy, New Delhi; Vikash Kumar Sinha, Associate Partner, Climate Change and Sustainability, MicroSave Consulting; Kunbihari Daga, Partner, Centre for Responsible Technologies, MicroSave Consulting; and Rahul Agrawal, Partner, Agriculture and Food Systems, MicroSave Consulting.
This was first published in “The Tribune” on 17th February 2026.
India stands at a one-of-a-kind threshold. Unlike traditional tech discourse, the upcoming AI Impact Summit 2026 is built on a well-rounded framework of seven interconnected chakras, which range from human capital to social empowerment. While computing capacity and data are vital, they are merely the starting point. The true test of inclusion and trust will occur only when artificial intelligence (AI) solutions hit the ground on scale. This is the same test that defined Aadhaar and the Unified Payments Interface (UPI). Now, success hinges on getting the fundamental components right and deliberately designing applications that translate the principles of people, planet, and progress into tangible global action.
Aadhaar and UPI did not succeed because they were technically perfect. Both systems faced early setbacks and public skepticism. They proved that large-scale digital systems could earn legitimacy if they improve everyday outcomes. They must also survive failure through strong governance. Aadhaar reduced leakages in welfare delivery and enhanced people’s experience in regular activities, such as banking and telecom. UPI removed friction in payments. People learned to trust these systems because they failed predictably and could be corrected without permanent exclusion.
India’s AI systems will face the same test but with higher stakes. Traditional digital systems are deterministic. The system denies access if a name, demographic details, or some other parameter does not match exactly. Much of the digital exclusion in India emerged when rigid rules collided with messy lives at the point of transaction. AI changes this dynamic because it is probabilistic. It can assess likelihood and context rather than demand perfect matches. This capability allows AI to function as an exception management layer. In theory, this makes AI a powerful tool to reduce exclusion.
This potential remains conditional on localised relevance and strong governance. Systems trained primarily on data from the Global North often struggle with local contexts and languages. In sectors, such as agriculture, a poorly translated advisory can lead to harmful instructions. For example, a system that translates content from English to Hindi might tell a farmer to “bury” a seed rather than “sow.”
Such errors quickly break fragile trust because a farmer knows that seeds are never buried. In a local context, burial may carry an altogether different meaning associated with finality rather than growth. Users at the margins view technology through the lens of rational risk management. In their world, a nonsensical instruction is a signal of total system unreliability rather than a minor glitch. When the stakes involve livelihoods and food security, even a single such alien output can cause users to abandon the technology permanently in favor of human intermediaries they know.
India’s readiness must be measured by institutional capacity, not infrastructure alone. Currently, the global community, especially the Global South, lacks sector-specific regulations, validation, and certification standards for AI solutions in critical areas, such as health, education, agriculture, and finance. This gap is untenable when AI mediates access to food and identity. The Global South risks a new era of digital imperialism without data sovereignty, where a few powers hold all control.
Full automation or intelligence in public systems is Utopian. A human in the loop is essential to inclusion. AI should support decisions and flag anomalies. Final accountability must remain human for at least the next decade for vulnerable populations. AI holds immediate value when it strengthens frontline workers, which includes banking correspondents, frontline health workers, and agricultural extension workers, who already command social trust. When these actors use AI tools, inclusion improves without forcing direct adoption on those least comfortable with it. Trust flows through people before it flows through the machine.
The global significance of this approach cannot be overstated. The UN Governing AI for Humanity report (2024) states that high-income countries are likely to see a 70% acceleration in AI discoveries during the next three years. For the Global South, that figure is only 30%, with a maturity gap that could take 10 years to close. India provides a blueprint to navigate this gap without falling into new forms of digital dependency. Success will not be defined at summits or in benchmarks. It will be decided quietly in clinics, ration shops, and farms. India’s AI moment will be remembered as a breakthrough only if its systems become as dependable as the human-centric processes they seek to support. Inclusion and trust will decide whether this decade is a breakthrough or a missed opportunity.
India has the opportunity to lead the Global South in the responsible and inclusive adoption of AI. The nation has done this before when it built world-class digital public infrastructure (DPI) at home, then made it a global movement by proactively sharing lessons and technology with the world. Will India be able to repeat the story in AI?
This was first published in “Hindustan Times” on 3rd February 2026.
National ID systems have evolved from administrative registries into core public infrastructure that underpins access to welfare, finance, healthcare, and education, as well as a growing range of digital services. During the past decade, artificial intelligence (AI) and data science have shaped how these systems are built and operated. In most countries, adoption of AI in national ID programs has remained narrow, focused on immediate scale challenges, such as biometric deduplication, authentication security, and fraud control. This limited adoption is often due to constraints in institutional capacity, legal and reputational risk, and rigid public procurement models.
These factors lead governments to favor proven, high-certainty AI applications, such as biometric de-duplication, over experimental or adaptive systems. Few national ID systems have deployed AI across the full identity lifecycle at scale. Beyond biometric de-duplication and liveness detection, most systems lack this capability due to governance, institutional, and risk constraints rather than technical feasibility.
While these applications are necessary, they are no longer sufficient. As national ID programs mature, identity systems must respond to new pressures, which include changing population characteristics, rising transaction volumes, evolving threat models, and increasing citizen expectations. For this analysis, we divide countries into three groups based on the maturity or life stage of their ID systems.
Stage 1 countries are currently establishing a national ID system or expanding coverage among populations that remain undocumented or underserved. These countries include Nigeria, Rwanda, and Ethiopia. Stage 2 countries have achieved high enrollment coverage and operate a mature foundational registry, and they include the Philippines, India, Thailand, and Indonesia. Stage 3 countries, such as Singapore and Estonia, operate highly mature national ID systems with deep integration across public and private services.
These stages are not sequential. Most national ID systems span multiple stages simultaneously, as individual components mature at different rates. These components include citizen enrollment coverage, use of digital ID for access to services and programs, integration with law enforcement, and the robustness of technology and scale. The strategic question for governments today is not whether to use AI, but how to align it with the country’s identity system maturity.
Stage 1: Building and saturating a foundational ID system
For countries in stage 1, the core objective is to scale with integrity. National ID authorities must onboard millions of residents and ensure that everyone is enrolled only once, often in contexts with limited infrastructure, weak connectivity, and incomplete population data. Further, a dominant systemic risk is exclusion. Individuals cannot enroll or repeatedly fail biometric or demographic quality checks, which results in denial or delay of downstream services.
Current application 1: Automated biometric identification systems
The technological backbone of ID systems is the automated biometric identification system (ABIS). It uses machine learning (ML) to compare fingerprints, facial images, and iris scans across population-scale datasets. ABIS helps establish an individual’s identity under two functions. One is identification, which searches for the submitted biometrics in the entire database, often referred to as a 1:N match. The other is verification, where it tries to match the submitted biometrics against the single record of the same person in the database, also called a 1:1 match.
India’s Aadhaar program illustrates the centrality of ABIS. The Unique Identification Authority of India (UIDAI) governs Aadhaar. It requiresmultimodal biometric capabilities to process millions of data packets daily for over a billion people with a minimal error rate. This multimodal approach ensures inclusiveness through alternative biometrics if a particular one cannot be captured or registered. For example, the use of iris or face if hands are amputated or fingerprints worn away by manual labor. However, population-scale ABIS increases sensitivity to capture quality, thresholds, adjudication, and exception handling. These are areas that can drive exclusion if poorly governed.
Current application 2: Optical character recognition with natural language processing (NLP) to extract physical information
Foundational ID systems often stall before they reach maturity, not due to lack of vision, but due to enrollment challenges. Governments must first enroll every eligible resident to unlock downstream value, but this task remains complex. Authorities must reach remote populations, capture accurate demographic and biometric data, navigate language barriers, and ensure inclusion for groups with limited digital access. In several countries, enrollment still relies on paper forms and assisted registration. Operators manually transcribe details and capture biometrics through shared devices, often under time and capacity constraints.
These conditions create predictable risks, such as limited training, low awareness, and manual data entry. These risks frequently lead to missing or inconsistent information that weakens the integrity of the ID system. One emerging solution is to use optical character recognition (OCR) and NLP to digitize handwritten forms at scale. Ethiopia, for example, is developing an AI-powered solution that reads consent forms and auto-populates digital records, which reduces manual errors and speeds processing across multiple languages. However, such tools must include strong validation checks and well-trained models. Without safeguards, automation can amplify errors, compromise data quality, and unintentionally exclude the very populations these systems seek to serve.
Opportunity 1: Edge AI for biometric quality enhancement
Poor biometric capture is a major source of exclusion. Glare, dirt on sensors, motion blur, or improper positioning often result in low-quality biometric images. Many systems detect these issues only after data reaches the central server, which triggers deferred rejection that forces citizens to repeat enrollment. This increases costs, prolongs onboarding, and raises higher dropout rates.
Edge-based AI models can address this challenge through quality control at the point of capture. On-device deep learning models can assess image quality in real time, verify compliance with international standards, and provide capture coaching for operators and citizens. Advancements in generative adversarial networks (GANs) and diffusion models have theoretically been successful in fingerprint enhancement and reflection removal from iris scans, among other applications. The national ID system authorities can enable this approach when they enforce device standards and support local quality checks. Edge AI transmits biometric data only after it meets quality thresholds, and deep learning models enhance image quality at capture. This combination could reduce re-enrollment, lower backend processing costs, and improve privacy through minimization of unnecessary data transmission.
Though the performance of these techniques depends on field conditions, such as camera quality, lighting, and device constraints, such emerging technologies offer potentially viable solutions for biometrics capture. Further, the deployment of edge AI in enrollment and authentication workflows would also require certified devices, controlled model and version rollouts, and audit logs to support accountability and dispute resolution.
Edge AI-enabled enrollment devices entail higher upfront costs, but they can materially reduce downstream expenditures. This reduction occurs as such devices lower re-enrollment rates, manual adjudication, and grievance handling, particularly in remote or high-error contexts. Authorities must limit this edge intelligence to capture quality assessment and operator guidance, preserve raw biometric data, and log all transformations to avoid compromise of evidentiary integrity.
Opportunity 2: Geospatial intelligence for outreach planning
A persistent weakness in early-stage ID programs lies in the enrollment outreach plan. Traditional approaches rely on administrative boundaries, electoral rolls, or census data that may be outdated by several years. Informal settlements, migratory populations, and sparsely populated rural areas are often undercounted or entirely missed, which leads to structural exclusion that is difficult to reverse later.
ML-assisted geospatial intelligence offers a way to overcome this limitation. AI models can estimate population distribution at a finer spatial resolution through satellite imagery, settlement extraction algorithms, mobile network coverage data, and road networks datasets. These models can identify exclusion hotspots, predict enrollment demand, and generate optimized routes for mobile enrollment units.
The GRID3 initiative in Nigeria demonstrates the practical value of this approach. GRID3 enabled authorities to plan malaria campaigns in locations where affected people lived with high-resolution satellite imagery and population estimates, rather than where administrative maps suggested.
National ID authorities can adopt similar settlement layers to identify and plan enrollment campaigns in remote or rapidly growing peri-urban areas, which ensures that coverage targets translate into real inclusion. Further, the use of open-source datasets, such as Meta’s AFD datasets, for population estimation enables quick development and reduced friction in consent boundaries.
Stage 2: Driving usage and safeguarding integrity
For countries in stage 2, the strategic focus shifts from onboarding to usage. As national IDs increasingly function as trust anchors for welfare delivery, financial inclusion, telecommunications, and digital services, risk shifts from exclusion to abuse at scale. Such abuse includes spoofing, coercion, and insider fraud, which could potentially erode trust. As transaction volumes grow, systems must remain reliable, secure, and interoperable.
Current application: Liveness detection and anti-spoofing
As authentication expands to mobile and remote channels, most Stage 2 countries deploy liveness detection algorithms. They focus on facial recognition to counter presentation attacks, such as photographs, masks, or deepfake videos. These techniques have become a baseline requirement for secure digital transactions. However, liveness detection algorithms often introduce false positives and accessibility challenges, especially for the elderly.
Opportunity 1: Graph neural networks (GNNs) for fraud detection
Traditional fraud detection relies on linear rules, such as transaction frequency thresholds or static blacklists. While effective against simple abuse, these approaches struggle to detect coordinated fraud that involves networks of identities, devices, or operators.
GNNs shift the analytical focus from individual transactions to relational structures. GNNs can detect structural anomalies that indicate coordinated misuse without the inspection of personal attributes by modeling anonymized identity graphs, such as device-to-user or operator-to-location networks. Graph or ML risk scores can trigger review or step-up verification within rights-sensitive ID systems to improve governance.
Graph-based fraud detection is proven at scale in the financial sector, most notably by PayPal, where graph models identify coordinated fraud rings that evade rule-based systems. Governments can adopt this proven approach for national ID systems. However, constraints around data availability, legal mandates, technical capacity, and governance safeguards challenge practical deployment. Further, the introduction of ML-based approaches should be integrated with the transparency and appeal mechanisms that are essential and integral for a national ID program.
Opportunity 2: Multilingual name normalization and match algorithms
In linguistically diverse societies, name variation is a pervasive but underappreciated source of exclusion. Differences in transliteration, spellings, and naming conventions often cause legitimate users to fail database-matching checks, even when their identities are valid. These failures generate manual exceptions and delay service delivery.
Transformer-based transliteration models and phonetic embeddings can normalize names across scripts and languages, which produce culturally aware canonical representations. Match scores allow systems to distinguish between likely variants of the same name and true mismatches, which enable fuzzy matching without loss of accuracy. Thus, the normalization of names can improve interoperability across civil registration, banking, and welfare systems, which reduces exception handling and does not force citizens to conform to a single representation of identity.
AI4Bharat’s IndicTrans model illustrates this capability through high-quality transliteration across Indian languages. The Indian judiciary already uses this model in initiatives, such as the Supreme Court Vidhik Anuvaad Software (SUVAS). Integration of these models into identity systems could improve interoperability across civil registries, banks, and welfare platforms, which reduces manual adjudication and improves user experience.
Stage 3: Enhancement of citizen experience and system intelligence
For countries with highly mature national ID programs, the binding constraint shifts to public trust and legitimacy, as citizens expect reliability, transparency, and fast resolution when failures occur.
Current application: Reactive analytics
Most advanced systems rely on dashboards that track failures, throughput, and performance indicators. Key signals include rising authentication failure rates in specific cohorts, growing exception volumes, and grievance backlogs. These interventions are typically reactive, triggered after a transaction fails or a grievance is lodged.
Opportunity 1: Identity lifecycle forecast
Biometric attributes degrade over time due to aging, occupational wear, and environmental factors. Documents expire, and demographic attributes change. Today, these issues often surface only when a citizen attempts a transaction and is denied service.
Survival analysis and time-series models can forecast when specific cohorts are likely to experience biometric or credential failure. Through analysis of historical authentication logs and update patterns, systems can prompt proactive updates before failures occur. This shifts identity management from a fail-and-fix approach to a proactive, predict-and-prevent model.
Opportunity 2: AI-powered grievance redressal
As ID systems grow more complex, citizens struggle to navigate static FAQs and overloaded call centers. Conversational AI systems based on LLMs can provide context-aware support in local languages, which explains errors, guides next steps, and resolves routine issues instantly.
For instance, Ethiopia deployed a local-language AI chatbot to support citizen interaction with their national ID system. The chatbot delivers accurate, multilingual responses across major digital platforms, which reduces response times, manual workload, and misinformation. In the context of national ID systems, LLMs with retrieval-grounded responses can prevent policy hallucination. Clear escalation to human agents for high-stakes cases and full audit logs can significantly improve grievance resolution times and citizen trust.
Conclusion
The future of national ID systems lies not in the deployment of more AI, but in the right intelligence based on the stage of maturity. Stage 1 systems must prioritize inclusion and data quality. Stage 2 systems must focus on interoperability and integrity. Stage 3 systems must emphasize resilience, anticipation, and citizen experience. These stages are not standalone technology upgrades. Authorities must sequence them with legal frameworks, institutional capacity, and operational readiness. When deployed prematurely or without adequate governance capacity, AI can amplify exclusion risks and erode trust in identification systems. The risk is especially high when systems use ID to determine eligibility or access to essential services.
With mature and aligned AI and data science, governments can transform national ID systems from static registries into intelligent public infrastructure. This infrastructure can maintain itself, remain accurate, inclusive, and trusted over time.
This site uses cookies, by continuing your navigation, you agree with our Cookie Policy.
