Real-time data, combined with digital KYC, further reduces onboarding costs. AI techniques, such as dynamic cluster analysis (which enables micro-segmentation) and propensity scoring, enable better targeting and customer retention.
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In the 1990s and 2000s, the conversation around financial inclusion focused on providing access to digital technology. Over the past decade, technology has revolutionised financial services, transforming them from traditional brick-and-mortar models to digital-first operations. Enabled by Digital Public Infrastructure (DPI), specifically components of India Stack—built on principles of security, interoperability, open standards, and legal frameworks—India has made significant strides in financial inclusion.
But having achieved broad-based financial inclusion, the key question today is: Can we transition from financial inclusion to financial integration? This evolution involves more than access—it aims to create a deeply interconnected financial ecosystem with customised solutions, even for those historically excluded from the formal financial system. By combining the scalability of India Stack with the intelligence of (AI), India could accelerate its digital transformation and address persistent financial access challenges.
In this article, we refer to AI technologies as a suite of advanced capabilities, including machine learning (ML), natural language processing (NLP), generative AI (GenAI), and advanced analytics.
According to McKinsey, AI may unlock $200–340 billion in annual value (2.8–4.7 percent of industry sales), primarily through productivity gains. More importantly, AI is poised to be a game-changer—not only for wealth or mid-market segments but also for underserved populations. AI can augment the efforts of financial institutions by transforming operations, enabling the creation of innovative financial products, and reducing fraud loss via robust monitoring systems. When combined with India Stack and appropriate regulatory frameworks, AI can drive impact across the full financial inclusion value chain.
India Stack’s first layer—the Identity Layer, built on Aadhar—enables paperless and presence-less Know Your Customer (KYC) verification. For financial institutions, Aadhaar has reduced client acquisition costs from $12 to just 6 cents (according to the IMF). Additional regulatory innovations, such as Central KYC or Video KYC, have further eased onboarding, allowing AI-based facial recognition algorithms to optimise both cost and user experience.
The second layer is the Payments Layer. India’s Unified Payment Interface (UPI) has revolutionised payments, offering seamless digital transaction capabilities that help build credit footprints. Financial institutions can now access transaction data via UPI and leverage the third layer: the Account Aggregator (AA) framework. As of February 2025, the AA framework has 112 million accounts linked and 100 million data-sharing consents.
Also read: From UPI to ONDC: The role of centralised orchestration in DPI success
However, banking data alone is not sufficient to design personalised, inclusive financial products. The Digital Personal Data Protection Act (DPDPA) 2023 supports consent-based data sharing from non-regulated entities. With DPDPA, social media platforms, e-commerce firms, and farmer-producer organisations (FPOs) can securely share data with financial institutions. The Act empowers citizens to decide how their data is collected, used, and shared, enhancing trust. For organisations, it ensures clear compliance while fostering innovation.
Historically, financial institutions have relied on standardised, one-size-fits-all products that often fail to meet the needs of the underserved. DPI and data-sharing frameworks now enable access to diverse datasets, including financial and non-financial data, as well as lifestyle data. AI can leverage these insights to perform nuanced risk assessments, offering valuable information about spending behaviours and income flows, which enables the design of tailored, cost-effective products.
Real-time data, combined with digital KYC, further reduces onboarding costs. AI techniques, such as dynamic cluster analysis (which enables micro-segmentation) and propensity scoring, enable better targeting and customer retention. AI-based voice assistants and chatbots can offer real-time, multilingual support, improving financial literacy and user confidence. These technologies also help reduce operating costs in areas such as customer service, fraud prevention, and complaint resolution. Crucially, regulators also leverage AI to monitor social media for early signs of predatory pricing or unethical practices, thereby protecting citizens and enabling evidence-based policymaking.
There is no reason why people with low incomes should receive poor-quality financial products. India’s robust DPI, supported by regulatory frameworks, provides a powerful platform to harness AI in closing the financial inclusion gap. However, the real question remains: Will AI be used to widen economic divides or to build a fairer, more inclusive, and equitable financial system?
While policymakers have laid the groundwork, the onus is now on financial institutions. Their choices will determine whether India achieves genuine financial integration—or merely stops at inclusion.
About the authors: Ashish Desai is an Associate Professor of Information Management and Analytics, and Abhiram R. is a PGDM student, both at the S.P. Jain Institute of Management and Research (SPJIMR).
Views are personal.
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[This article has been reproduced with permission from SP Jain Institute of Management & Research, Mumbai. Views expressed by authors are personal.]
