AI can transform India’s lending ecosystem by expanding credit access through alternative data, but equitable inclusion requires transparency, accountability, and safeguards against algorithmic exclusion
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Resource allocation and utilisation shape development outcomes in meaningful ways. As artificial intelligence becomes more embedded in economic systems, understanding its role in supporting country-specific development priorities becomes increasingly important.
Despite remarkable progress in India’s digital infrastructure and private-sector innovation, financial inclusion remains an unfinished project. Decades of policy interventions have narrowed the gap, but millions remain outside the formal financial system. The advent of AI and other emerging technologies offers an opportunity to accelerate inclusion at scale, improve service delivery, and reduce the frictions that have long constrained access. If harnessed wisely, technology could help India complete a journey it has long been on: building a more inclusive and equitable economy.
A decade ago, financial inclusion efforts in India were primarily centred on expanding access to formal banking services, most notably through the mass opening of bank accounts. However, the contemporary paradigm has evolved significantly to encompass the broader objective of economically empowering individuals, enterprises, and communities through integrated access to credit, digital payment systems, and social protection mechanisms. These developments underscore a shift from mere financial access to meaningful financial participation. India’s financial inclusion journey must now prioritise access to credit.
The Reserve Bank of India’s Financial Inclusion Index (FI-Index) indicates steady progress in deepening financial inclusion nationwide, rising to 67 in 2025—a 24.3 percent rise since 2021. The index, which is constructed from three sub-indices—access, quality, and usage—suggests that while physical and digital access to financial services has expanded and service quality has improved, usage continues to lag. This gap underscores a persistent challenge: the mere availability of financial accounts does not automatically translate into meaningful financial engagement.
A bank account should serve as the foundational gateway to a broader range of financial services, including credit, insurance, pensions, and investment products. However, without sustained utilisation of these products, financial inclusion risks remain largely nominal, failing to deliver the full developmental and economic benefits of a well-functioning financial system. Addressing the “usage gap” will require targeted interventions that go beyond infrastructure provision, focusing on financial literacy, trust-building, and the design of products that are relevant and accessible to underserved populations.
Small borrowers previously excluded from formal credit due to the absence of a verifiable financial history can now be assessed through standardised digital interfaces that aggregate consent-based data across institutions.
India’s financial inclusion agenda has progressed from simply expanding access to banking to fostering meaningful economic participation. Today, integrated access to credit, digital payments, and social protection is driving this shift. With over 520 million Jan Dhan accounts, UPI processing around 18 billion monthly transactions, and sustained credit growth in MSMEs and agriculture, the focus is increasingly on enabling credit as a cornerstone of inclusion. Open credit networks such as the Open Credit Enablement Network are restructuring the architecture of lending markets by establishing shared protocol layers on which borrowers, lenders, and intermediaries can interact without bilateral integration. Small borrowers previously excluded from formal credit due to the absence of a verifiable financial history can now be assessed through standardised digital interfaces that aggregate consent-based data across institutions.
Credit remains a cornerstone of financial systems, and the evaluation of personal credit is central to this process. In this context, the integration of artificial intelligence into lending practices has emerged as a transformative development. Contemporary AI models process vast datasets encompassing borrowers’ payment histories, income patterns, expenditure behaviours, and alternative data sources such as utility bills and mobile payment records. These models enable the construction of highly accurate credit scores and risk profiles, thereby mitigating human bias and facilitating near-instant decision-making. Such innovations are particularly beneficial for ‘thin-file’ borrowers, who lack traditional credit histories and are often excluded from formal credit systems.
For instance, a notable advancement is the adoption of decision tree–based algorithms for credit evaluation. Although relatively recent in their application to financial services, decision trees have introduced a structured and systematic framework for assessing creditworthiness. By distinguishing between ‘good credit’ and ‘bad credit’ applicants using sociodemographic variables, repayment behaviour, and loan characteristics, these models provide a more granular understanding of risk. Their tree-like architecture, which maps potential outcomes and weighs multiple dimensions of credit history, offers institutions a transparent and data-driven mechanism for informed loan approvals and enhanced risk management.
Artificial intelligence systems are increasingly deployed to continuously monitor borrowers’ financial activities, enabling early detection of deteriorating creditworthiness. This enables lenders to proactively mitigate risk exposure and minimise potential losses. Beyond monitoring, AI-driven agents also perform operational functions such as parsing loan-related documents, verifying applicant information, cross-referencing data with credit bureaus, and dynamically retraining predictive models based on the most recent datasets.
The capacity of AI to deliver transparent and data-driven credit assessments is particularly significant for the micro, small, and medium enterprises (MSMEs) sector, which has historically faced barriers to accessing formal credit due to its reliance on conventional, documentation-heavy lending processes. In India, several non-banking financial companies (NBFCs) and fintech firms now employ AI algorithms capable of rapidly analysing MSME loan applications by drawing on diverse datasets, including transaction histories, GST filings, utility bill payments, business performance indicators, and even digital footprints such as social media activity. By leveraging such multidimensional data, these systems expand credit access for enterprises with limited or non-existent traditional credit records.
Government-supported platforms such as PSB Loans in 59 Minutes illustrate the potential of AI-enabled lending infrastructure to provide near-instant approvals. Such innovations are particularly impactful for MSMEs in tier-2 and tier-3 cities, as well as underserved sectors, thereby helping to bridge longstanding credit access gaps and promoting inclusive financial growth.
Beyond the widely acknowledged advantages of AI, including enhanced data processing capabilities, task automation, and operational cost optimisation, its transformative potential in financial service delivery lies in enabling seamless, localised, and inclusive user experiences. Social media and communications executives have underscored this potential in the context of service delivery, noting that the integration of conversational AI with digital payment infrastructure such as UPI is enabling frictionless financial interactions, particularly in vernacular languages. This, they suggest, points to a future in which linguistic accessibility becomes a critical determinant of fintech adoption.
AI is not a panacea. Yet, if deployed with intent and equity at its core, it can become a powerful catalyst for financial inclusion.
Amitabh Kant, India’s former G20 Sherpa, reinforces this perspective by emphasising the significance of data-driven credit systems. He argues that the future of credit access will be predicated on frameworks that are paperless, presence-less, and enriched with alternative data sources. Within such a paradigm, AI possesses the potential to democratise credit allocation by enabling creditworthiness assessments that transcend conventional scoring mechanisms such as CIBIL.
Crucially, the convergence of AI, alternative data, and interoperable payment systems can address structural barriers that have historically excluded large segments of the population from formal finance. By leveraging behavioural, transactional, and contextual datasets, AI-driven models can more accurately capture the economic realities of underserved users, thereby expanding the contours of financial inclusion. In the long term, such advancements could not only reshape the economics of credit delivery but also foster more resilient, adaptive, and inclusive financial ecosystems.
The issue is largely structural. Many AI systems used in lending, insurance, and wealth management operate with limited transparency, producing outcomes that users often cannot easily understand, question, or challenge. This can lead to what might be described as “silent exclusion”, where individuals are affected by automated decisions without clear explanations or avenues for recourse. While regulatory safeguards exist, including the RBI’s norms on digital lending and SEBI’s rules for algorithmic trading, there is still no binding requirement for explainability, inclusion checks, or independent oversight. As a result, there is a risk that existing biases may be reinforced, even as these systems are framed as innovation-driven solutions.
A useful benchmark for evaluating AI in finance may be whether it helps reduce existing gaps in access and opportunity, rather than unintentionally deepening them.
AI is not a panacea. Yet, if deployed with intent and equity at its core, it can become a powerful catalyst for financial inclusion. Real progress will depend on building AI capacity across underserved institutions, including small finance banks, cooperative credit societies, and rural banks, through dedicated grants, targeted training, and public–private partnerships. It will also require a broader definition of innovation: success cannot be measured by technical sophistication alone, but must also account for social impact. Inclusion indices and outcome-based metrics should therefore be embedded within regulatory sandboxes and pilot projects to assess whether new solutions meaningfully benefit those at the margins.
Policymakers and regulators could also place greater emphasis on encouraging technologies that show clear improvements in outcomes for women, marginalised groups, gig and platform workers, and people living in remote or underserved areas. A useful benchmark for evaluating AI in finance may be whether it helps reduce existing gaps in access and opportunity, rather than unintentionally deepening them. If applied thoughtfully, AI has the potential to strengthen trust in the financial system while supporting India’s wider objectives of inclusive and sustainable growth.
Tanusha Tyagi is a Research Assistant at the Centre for Digital Societies, Observer Research Foundation.
Sauradeep Bag is an Associate Fellow with the Centre for Security, Strategy, and Technology at the Observer Research Foundation.
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Tanusha Tyagi is a research assistant with the Centre for Digital Societies at ORF. Her research focuses on issues of emerging technologies, data protection and ...
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Sauradeep is an Associate Fellow at the Centre for Security, Strategy, and Technology at the Observer Research Foundation. His experience spans the startup ecosystem, impact ...
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