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The intelligent use of AI and data science in the lifecycle of national identity systems

Posted on by harshita

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 requires multimodal 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. 

Union Budget 2026: MSC’s expert analysis

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Leadership quotes on India’s 2026 Union Budget announcement:

Abhishek Anand, Senior Partner – Banking Financial Services and Insurance, on “The Daily Jagran”

“Refinancing ease for NBFC-MFIs is important for micro enterprise growth and the ultimate growth of the Viksit Bharat goal ahead.”

Vikash Sinha, Associate Partner – Climate Change and Sustainability, on “India CSR”

“Climate adaptation needs dedicated local provisions and said the allocation of INR 1.4 lakh crore under the 16th Finance Commission is commendable and strengthens fiscal federalism. Also, climate resilience requires dedicated provisions for locally led adaptation mechanisms that can crowd in private innovation and investment.”

Puneet Khanduja, Associate Partner – Health and Nutrition, on “Money control”

“Such announcements create a strong platform for systemic reforms. However, their impact will depend on effective state-level implementation, regulatory alignment, and integrated models that translate policy intent into actionable institutional expansion.”

Rajnish Kumar, Senior Manager – Agriculture and Food System, on “Nuffoods Spectrum”

“The Union Budget 2026–27 signals a decisive shift toward productivity-led agricultural growth. The emphasis on integrated fisheries and livestock value chains, development of 500 reservoirs and Amrit Sarovar lakes, and AI-enabled productivity through Bharat Vistaar with AgriStack–ICAR integration aligns well with the Economic Survey’s diagnosis. The real test will be rapid execution and demonstrable gains in productivity and farm incomes.”

Ayushi Mishra, Senior Manager – Communities and Livelihoods, on “Business world”

“Diversification must extend beyond crops to livelihoods. Aligning community livelihoods with climate-resilient agriculture could transform sustainability into a scalable economic strategy. Integrating self-help groups into formal value chains and promoting green jobs could strengthen rural resilience while aligning growth with India’s climate commitments.”

Rajarshi Dutta Barua, Associate Partner – Banking Financial Services and Insurance, on “CNBC”

“The ₹10,000 crore SME Growth Fund is a decisive intervention to create future champions by providing equity support and risk capital to promising MSMEs. It addresses critical equity gaps that prevent small enterprises from scaling globally and can be strengthened through faster settlements on platforms like TReDS and expanded credit guarantee coverage for first-time women and marginalised entrepreneurs.”

Agriculture in Budget: Why the next leap must be strategic, not incremental

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As the Union Budget for 2026–27 approaches, agriculture has been articulated as the first engine of development. According to the Periodic Labour Force Survey (2023–24), agriculture and allied activities employ 46.1 per cent of India’s workforce, underscoring the sector’s centrality to livelihoods, food security, and rural demand. The question before policymakers, however, is not one of intent or aggregate spending, but of whether budgetary choices are aligned with the sector’s structural needs.

Budgetary allocations to the Department of Agriculture and Farmers’ Welfare (DA&FW) have expanded significantly in nominal terms, from ₹21,933 crore in FY 2013–14 to ₹1.27 lakh crore in the Budget Estimates for FY 2025–26. Agriculture-related spending also flows through multiple ministries, covering irrigation, renewable energy, fertilisers, rural employment, and research. This reflects sustained fiscal attention and an increasingly whole-of-government approach.

However, relative prioritisation tells a different story. DA&FW’s share in the total Central Plan outlay has declined steadily, from 3.53 per cent in 2021–22 to 2.51 per cent in 2025–26. Actual expenditure has frequently undershot budget estimates, particularly during fiscally constrained years. While allocations remain large, agriculture’s relative weight within the expanding public expenditure framework is diminishing.

Persistent productivity challenge

This trend is significant because Indian agriculture continues to face a persistent productivity challenge. Despite employing nearly half the workforce, the sector contributes less than one-fifth of GDP and exhibits lower growth than the rest of the economy. Incremental increases in outlays, without changes in spending composition, are unlikely to alter this imbalance.

A review of recent budgets shows a continued concentration of resources in income support, input subsidies, and risk mitigation schemes. PM-KISAN, for instance, has improved income predictability and strengthened direct state–farmer linkages through digital transfers. The expansion of Aadhaar-linked delivery systems and the rollout of AgriStack represent genuine improvements in targeting, transparency, and administrative capacity. These initiatives have strengthened the welfare architecture and merit acknowledgement.

But welfare efficiency is not the same as productivity enhancement. The binding constraints in Indian agriculture lie in weak seed systems, inefficient water use, deteriorating soil health, limited extension capacity, post-harvest losses, and poor market integration. Budgetary priorities continue to underweight these productivity-enhancing public goods relative to recurring expenditures.

Complementing with sustained budgetary support

Seed systems provide a useful illustration. Yield stagnation across several crops reflects slow varietal turnover, uneven quality assurance, and limited diffusion of improved genetics. The draft Seed Bill recognises this institutional gap by proposing reforms to certification, quality control, and innovation incentives. However, regulatory reform must be complemented by sustained budgetary support for agricultural research, adaptive trials, extension networks, and farmer adoption to generate measurable gains.

Risk mitigation schemes show a similar pattern. Crop insurance and credit-linked support absorb substantial resources but remain constrained by delayed settlements, uneven coverage, and weak alignment with actual production risks. Without complementary investments in irrigation, climate-resilient practices, and localised extension, these schemes function primarily as ex-post compensation rather than ex-ante resilience-building tools.

Input subsidies, including fertiliser support, further illustrate the tension. While digital monitoring has improved transparency, distorted price signals continue to encourage inefficient input use and impose high fiscal costs. Policy discourse increasingly recognises the need for rationalisation, but progress remains incremental. Reform must be sequenced and linked to productivity, soil health, and income outcomes, not treated as a narrow fiscal correction.

Public investment gaps

Public investment gaps are most evident in post-harvest infrastructure, storage, processing, logistics, and value-chain integration. Persistent post-harvest losses, particularly in horticulture, livestock, and fisheries, continue to erode farm incomes. Instruments such as the Agriculture Infrastructure Fund have demonstrated demand, but their effectiveness depends on last-mile execution and stronger integration with farmer producer organisations and markets.

The broader lesson is clear. India has built an extensive safety net for agriculture, but a weak ladder for growth. Digital public infrastructure such as AgriStack offers an opportunity to rebalance this approach, enabling differentiated support, outcome-linked incentives, and accountability. But technology cannot substitute for strategic prioritisation.

If agriculture is truly to serve as India’s engine of development, the Budget must signal a shift, from income support to income generation, from fragmented schemes to coherent value-chain strategies, and from managing distress to enabling productivity-led growth. This does not require dramatically higher spending. It requires sharper choices. The foundations are in place. What is needed now is strategic follow-through.

This was first published in “The Hindu” on 31st January 2026.

From infrastructure to intelligence: Rethinking India’s health priorities in Budget 2026

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As the Union Budget 2026 approaches, the national discourse is increasingly anchored in the vision of Viksit Bharat 2047. For the health sector, this ambition now requires a clear shift from a primary focus on infrastructure expansion to the creation of a technology-enabled, patient-centric health ecosystem.

Budget 2026 must be framed as a health systems budget, not a healthcare spending budget. The required shift is from fragmented funding to predictable financing, from service expansion to system resilience, from treatment dominance to prevention and early detection, from technology pilots to national digital infrastructure, and from cost containment to innovation-led competitiveness.

India’s recent public health gains are significant. The elimination of trachoma in 2024 and the decline in the total fertility rate to 1.9 reflect decades of sustained investment and reform. The next phase of transformation must build on this foundation by enabling a second revolution: the large-scale integration of digital public infrastructure and artificial intelligence into public health delivery.

The digital mandate: universalising health data systems

A core expectation from Budget 2026 is accelerated saturation and deepening of the Ayushman Bharat Digital Mission. While over 84 crore Ayushman Bharat Health Accounts have been created, the true value of ABDM lies in interoperability rather than registration alone. Budgetary support is required to integrate vertical programmes—from maternal and child health to tuberculosis and non-communicable diseases—into a unified digital stack.

Interoperable patient records are essential to ensure that medical histories move seamlessly across levels of care, from rural sub-centres to district hospitals and tertiary facilities. At the same time, the success of  eSanjeevani, which has enabled more than 44 crore teleconsultations, demonstrates both demand and feasibility. The next step is to expand specialist tele-services, including tele-radiology and AI-assisted diagnostics, to reduce diagnostic delays in underserved regions.

Artificial intelligence: from logistics to life-saving care

Artificial intelligence is emerging as a critical enabler of efficiency and responsiveness within the health system. Budget 2026 offers an opportunity to support pilots and scale initiatives that embed AI into core public health functions.

One priority area is supply chain management. AI-based demand forecasting within drug and vaccine distribution systems can reduce stock-outs and wastage, improving service continuity and planning. Another important frontier is drone-enabled logistics. Scaling drone-based delivery of vaccines, diagnostics, and emergency medicines to remote and hilly regions is no longer experimental; it is essential for achieving health equity in hard-to-reach geographies.

Addressing the triple burden: NCDs and cancer care

India’s epidemiological transition has made the rising burden of non-communicable diseases a pressing fiscal and policy concern. Budget allocations must reflect this shift, particularly for cancer and chronic disease care.

Decentralized cancer treatment should be prioritised. Operationalising day care cancer centres in district hospitals would significantly reduce travel burdens and treatment disruptions for patients requiring chemotherapy. In parallel, preventive care must be strengthened. Expanding population-level screening beyond diabetes and hypertension to include conditions such as chronic kidney disease, chronic obstructive pulmonary disease, and fatty liver disease, especially among younger populations, will be critical to preventing long-term health and productivity losses.

Building a climate-resilient health system

Climate change is increasingly influencing disease patterns and service disruptions. Budget 2026 must respond by investing in climate-resilient and energy-efficient health facilities. Integrating climate risk assessments into existing maternal and child health programmes will be essential to strengthen preparedness for heat stress, vector-borne diseases, and climate-related health shocks.

Fragmented funding to predictable financing

This begins with shifting from scheme-by-scheme allocations to multi-year, predictable financing that states and providers can plan around. It also requires pooling and aligning funds across the Center and states, and across vertical programs, to reduce duplication and close last-mile gaps. Predictable financing should be tied to measurable outcomes—coverage, quality, and continuity of care—rather than only to inputs and infrastructure. Strengthening primary care and public health functions through stable operating budgets will be as important as capital expenditure. Finally, timely fund flows and streamlined procurement will determine whether digital systems, diagnostics, and frontline services scale reliably.

Conclusions

India’s health achievements to date are substantial. Maternal mortality has declined sharply since 1990, and out-of-pocket expenditure as a share of total health spending has fallen steadily over the past decade. Yet, reaching the global top tier of health systems will require a strategic pivot.

Budget 2026 must move beyond incremental change and invest in a whole-of-society approach that places digital public infrastructure, artificial intelligence, and preventive care at the centre of health policy. The opportunity now is to transition decisively from an illness-centric system to a wellness-led, digitally empowered healthcare future.

This was first published in “ET edge insights” on 30th January 2026.

Leveraging data sharing systems to improve public service delivery in LMICs

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This white paper explores data sharing as a key pillar of digital public infrastructure, which operationalizes interoperability in practice. It presents a broad conceptualization of data sharing systems that combines technology solutions, policies, and governance. These systems highlight trust and data protection by design as core features. This paper explores how these systems can improve public service delivery based on case studies from Brazil, Cambodia, Mauritius, and Uganda. Further, the paper outlines diverse models, use cases, challenges, and enabling factors. It also discusses emerging efforts to build open data ecosystems to drive AI innovation in public service delivery. This paper concludes with recommendations for governments and ecosystem stakeholders to scale data sharing systems for more accessible and efficient public services in LMICs. 

Reaching the unreached: Strengthening last-mile delivery for particularly vulnerable tribal groups (PVTGs)

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Gumla’s story highlights the importance of strengthening last-mile delivery of financial services for particularly vulnerable tribal groups (PVTG) through closer alignment with on-ground realities.

Across India’s diverse tribal landscape live 75 PVTGs, spread across 18 states and one union territory. They comprise around 2.8 million people. The Dhebar Commission first identified these communities as ‘Primitive Tribal Groups (PTGs)’ in 1973. The government reclassified them as PVTGs in 2006. These communities have low literacy levels, pre-agricultural technology, and subsistence-based economies. Often, they also have declining populations.. 

Over the past decade, national and state governments have intensified efforts to reach these people. The Pradhan Mantri Janjati Adivasi Nyaya Maha Abhiyan (PM-JANMAN) mission began in 2023 with an allocation in excess of INR 240 billion (approximately USD 3 billion).

The Aspirational Blocks Programme (ABP) by the NITI Aayog has generated renewed momentum in bridging historical gaps. Under ABP, block-level convergence mechanisms and locally adapted delivery approaches seek to align welfare systems with the lived realities of PVTG communities. Recent results include the completion of 136,000 houses and the establishment of piped water in more than 7,400 villages. These efforts also improved electricity and mobile connectivity.  

Delivery models of welfare, financial inclusion, and social protection services often work well when people have access to regular services and can navigate written or digital processes. They run smoothly when people have standard documentation, predictable income, and stable mobility patterns. While these conditions hold in some contexts, they do not align with settings shaped by remote geographies, seasonal livelihoods, linguistic diversity, and strong community-based social structures. 

MSC drew from field engagement and evidence to examine last-mile delivery of public and financial services for PVTG communities using the PACE lens, focusing on:

Delivery models encounter multiple constraints: distance, mobility, and predictability  

The first layer reveals that service delivery models are anchored in formal systems. In many PVTG habitations, engagement with public systems is difficult because settlements are dispersed across forested terrain, and their mobility is dependent on their livelihoods rather than access to services. Banking infrastructure data reflects this gap between service locations of banking touchpoints and everyday mobility patterns of PVTG households. In Maharashtra, for example, nearly half of the blocks with tribal population currently lack a bank branch or ATM, and only 58% of tribal women have active accounts compared to 78% statewide.  

As connectivity expands, these regions hold potential for greater participation in financial systems. Digital connectivity is gradually expanding access options. Under the 4G saturation initiative, the government covered 900 PVTG villages so far, and 500 mobile towers were installed in six months. Complementing this, BharatNet has connected more than 210,000 gram panchayats to expand digital pathways for welfare and financial services. This expansion, however, does not automatically translate into last-mile usage. In practice, whether digital services are used often depends on how transactions are perceived and socially validated at the community level. In a small market settlement of Tripura, merchants continue to prefer cash, since “with cash, the work feels complete,” as one shopkeeper noted. Seeing neighbors receive instant confirmations has helped digital tools feel increasingly dependable. Field interactions suggest that effective access depends less on services being available and more on transactions being socially verifiable- where people can see, hear, or confirm through trusted networks that payments have gone through.  

When communication systems rely on text, but engagement is built on trust  

Another area where service delivery becomes nuanced for PVTGs relates to how people receive and interpret information. Many public systems rely on written instructions, standard templates, and forms. In contrast, PVTG communities often prefer oral communication and rely on trusted interpersonal networks and familiar social settings.

PVTGs

MSC’s field experience from Muniguda block in Odisha illustrates how customized communication pathways shape engagement. In several PVTG habitations, information about banking and welfare services circulates primarily through verbal exchanges and trusted local contacts rather than written notices or formal announcements. Participation in service camps is higher when information is conveyed by familiar individuals, and lower when communication relies on impersonal or text-heavy formats.

Another hurdle is the local dialects that most households primarily speak. Literacy among PVTGs is estimated to be between 10% and 44%, whereas it is 74% nationwide. As a result, information may be technically available but not meaningfully accessible.

Initiatives, such as Bhashini, demonstrate how digital public infrastructure can support inclusive communication. This platform enables real-time translation across scheduled and tribal languages, both through speech and text. Such language-enabled platforms complement trust-based, oral communication by aligning language, medium, and messenger.

From awareness to enrolment: navigating processes on the ground

Once households decide to engage with public services, the nature of interaction shifts to process navigation. Evidence from a 10-district study in Jharkhand indicates that the Janani Suraksha Yojana did not cover 67% of PVTG women, compared to approximately 63% across India.  Despite the modest difference in coverage levels, the comparison draws attention to how documentation requirements and process complexity intersect with literacy, language, and mobility constraints in PVTG contexts.

Similar observations can be seen in community-led group spaces. In Chhattisgarh, women who attend financial literacy sessions prefer seating arrangements that align with local customs. This preference for familiar settings is echoed in PVTG habitations in Jharkhand, where community meetings are most effective in open spaces rather than offices. “Here, we feel more comfortable asking questions,” a member observed. When conversations with community members move at a pace shaped by group discussion and contextually relevant set-ups, participation deepens and understanding follows naturally.

Correspondingly, women’s participation in self-help groups in several PVTG geographies remains below 15% compared to 21% nationally.  These numbers highlight lower participation in formal group-based arrangements among PVTG women, pointing to the need for further analysis of how platform design and social context shape engagement.

Open-space community meetings in Dumri block, Jharkhand, support participation in PVTG habitations

Several states are responding to these contextual factors. In Gumla district, Jharkhand, a helpline operated by a PVTG community member combines trust with practical guidance and provides Aadhaar and documentation support in the local dialect. Models, such as BC Sakhis, locally tailored NRLM financial literacy campaigns, pictorial Information, Education, Communication (IEC) materials, and community radio, similarly contribute to improved comprehension and reduced hesitation. Alongside such measures, the district also adopted an Awareness–Enrolment–Troubleshooting (AET) approach, where information dissemination is complemented by enrolment assistance and support to resolve documentation or account-related issues that emerge during access. Rather than treating engagement as a one-time interaction, this approach recognises the need for continued handholding across stages in contexts shaped by literacy, language, and mobility constraints.

Taken together, these examples illustrate how enrolment processes are being adapted on the ground through local language support and familiar social settings in select states.

Livelihood rhythms and financial engagement

Livelihood and income further shape how households engage with financial and welfare systems. For many PVTG households, incomes are seasonal, informal, and linked closely to natural cycles. They earn from forest produce, marginal farming, or daily wage labor. Therefore, even small disruptions, such as illness or delayed payments, can hurt their financial stability.

These realities shape how households interact with formal financial systems. Products designed around regular incomes and fixed repayment schedules may not always align with seasonal cash flows. As a result, households may engage cautiously or intermittently, even when access is available.

Livelihood initiatives linked to shared production offer insights into between livelihood cycles and financial engagement mechanisms can support more stable engagement. In a premium indigenous rice variety, which linked them to better markets and more predictable incomes.

Similarly, in areas where women’s SHG federations are structured around seasonal earning patterns, monthly household incomes have increased by up to 22%. These experiences show how livelihood programs grounded in local economic rhythms can help households strengthen resilience and sustain engagement with formal systems.

Jeeraphool cultivation by PVTG farmers in Mahuadanr block, Jharkhand, reflects how collective livelihood platforms support more predictable incomes

Implications for policy and program design in PVTG contexts

In policy and program design, services for PVTG communities are more effective when delivery arrangements reflect local work patterns, mobility, and access constraints. Services are easier to access when they are available at right times and places, and when information is shared through trusted local channels and familiar languages. Participation is also shaped by seasonal livelihoods and limited financial buffers, which make it difficult for households to engage if services require repeated visits or loss of income. Engagement tends to be more sustained when local intermediaries support communities in navigating public systems and resolving issues over time. Across several PVTG areas, district administrations are beginning to adapt delivery approaches by combining improved access with clearer communication and practical process support, as reflected in initiatives such as PM-JANMAN and the Aspirational Districts and Blocks Programme. As a development partner under these programmes, MSC supports districts in identifying locally grounded models that help move from outreach to more consistent and meaningful participation.