Indian payments chief sees AI driving next era of digital payment growth

At a glance:

• NPCI targets over 1 billion daily UPI transactions with AI assistance for user growth, fraud prevention, and credit distribution
• AI will enable voice and multilingual onboarding solutions, building on NPCI's 2023 voice assistant launch
• PhonePe and Google Pay control over 80% market share despite planned 30% cap taking effect December 31, 2026

AI's role in expanding UPI's next phase

India's digital payment landscape has evolved dramatically, with the Unified Payment Interface (UPI) processing over 750 million daily transactions. With ambitions to reach over 1 billion daily transactions, Dilip Asbe, Managing Director and CEO of the National Payments Corporation of India (NPCI), envisions artificial intelligence as a cornerstone of this next growth phase. During an interview at Mumbai Tech Week 2026, Asbe outlined how AI would be instrumental in achieving this target, emphasizing its multifaceted applications across the payment ecosystem.

"AI will be used very effectively when we look at the next wave of UPI, and that includes all aspects, including reaching new users," Asbe explained. "We must use AI effectively to protect our current citizens, to find fraud, and to find mules. AI must also be used to provide credit to all the users and merchants who have digital footprints." His vision extends beyond simple transaction processing to encompass comprehensive financial inclusion through intelligent systems that can identify and serve previously underserved populations.

The integration of AI into UPI operations represents a strategic response to the rapid digitization of payments in India. As the system scales toward its ambitious billion-transaction milestone, traditional rule-based approaches become insufficient for managing complexity and ensuring security. AI systems can process vast amounts of transaction data in real-time, identifying patterns that suggest legitimate activity versus potential fraud or money laundering through intermediary accounts known as "mules."

Voice technology and multilingual accessibility

Beyond backend fraud detection and credit assessment, Asbe highlighted the importance of voice interfaces and multilingual solutions for simplifying user onboarding. His perspective aligns with broader trends in Indian technology adoption, where voice-based interactions are becoming increasingly important for reaching non-English speaking populations and users uncomfortable with text-based interfaces.

NPCI's early experimentation with voice assistant-based interactive systems began in 2023, though adoption rates have yet to meet expectations. Asbe noted that current voice models require greater accuracy before they can become integral components of the payment ecosystem. The potential remains significant, however, particularly for explaining transaction details, guiding users through complex financial products, and enabling hands-free payment authorization in contexts where visual interfaces are impractical.

The multilingual challenge is particularly acute in India's diverse linguistic landscape. Successful AI implementations must accommodate numerous languages and dialects while maintaining consistency in financial terminology and security protocols. Asbe suggested that with the right use cases identified and developed, voice technology could become a critical component in making digital payments truly accessible to all Indians regardless of literacy level or language preference.

AI in finance and regulatory frameworks

The momentum behind AI integration in financial services extends beyond India's borders. In the United States, companies like Coinbase and Robinhood now enable AI agents to execute trades on behalf of users, while OpenAI allows individuals to import personal account data into ChatGPT for financial guidance. NPCI has explored similar possibilities, demonstrating agentic commerce and payment capabilities alongside Razorpay last year. However, widespread deployment of these features remains limited in the Indian context.

Asbe emphasized that successful AI adoption in Indian finance requires robust regulatory frameworks that protect consumers while enabling innovation. Key considerations include establishing clear guidelines for user consent, ensuring transparency in automated decision-making, and creating accountability mechanisms when AI systems make errors or cause financial losses. In the event of system failures, regulators and financial institutions must be able to audit AI-driven transactions and understand the instructions provided to automated agents.

The regulatory approach must balance innovation incentives with consumer protection. Asbe advocated for frameworks that encourage experimentation while establishing clear boundaries for acceptable AI behavior in financial contexts. This includes defining scenarios where human oversight remains mandatory and creating standardized processes for reporting AI-related incidents to regulatory authorities.

Building indigenous language models

Recognizing the importance of culturally relevant AI capabilities, Asbe proposed that India's financial ecosystem has an opportunity to develop small language models tailored to local needs. He argued that model differentiation increasingly depends on the quality and specificity of available datasets, noting that India's financial system generates rich, diverse data that could fuel world-class indigenous AI development.

"We believe that the models will differentiate from each other based on the data sets that are made available to them," Asbe observed. "We have a very rich data set in our ecosystem. I think there is a big opportunity for Indian companies — the banks, FinTechs, and the ecosystem — to create small language models which are sharp, specific, and as deterministic as possible." This approach would contrast with larger, more generalized models that may struggle with India's linguistic diversity and unique financial behaviors.

NPCI's first step in this direction was the launch of FIMI, a specialized model designed to resolve user disputes and manage mandate cancellations. Since its introduction, FIMI has been serving over 1 million users and continues to scale rapidly, demonstrating the viability of focused AI applications in financial services. The success of FIMI suggests that targeted, well-defined AI use cases may prove more practical than attempting broad, general-purpose implementations.

Market concentration and regulatory intervention

Despite NPCI's efforts to foster competition among UPI-powered applications, market data reveals significant concentration risks. Walmart-owned PhonePe and Google Pay collectively command over 80% of the UPI market, leaving smaller players struggling for visibility and user acquisition. To address this imbalance, regulators have planned a cap limiting any single application's market share to 30%, scheduled to take effect on December 31, 2026.

Asbe acknowledged the competitive advantages that PhonePe and Google have built through substantial investment in their respective platforms. Both companies have leveraged their parent organization's resources to create feature-rich applications that attract and retain users. The low switching costs between UPI apps mean that users often remain with familiar platforms even when alternatives exist.

The availability of viable commercial models emerges as a critical factor in determining market dynamics. Asbe noted that without sustainable revenue streams, new entrants struggle to justify the heavy investment required for user acquisition and platform development. "The moment we see the commercial model being available to the ecosystem, I believe newer players will start investing very heavily," he predicted.

BHIM UPI and sovereign alternatives

In 2024, NPCI took the strategic step of spinning off its BHIM UPI app to make it more competitive in the marketplace. While transaction volumes for BHIM have grown since this restructuring, the app maintains only around 1% market share. Asbe clarified that NPCI does not have a specific market share target for BHIM, instead positioning it as a sovereign and secure alternative to commercial applications.

The BHIM initiative reflects broader concerns about data sovereignty and platform dependency in India's digital payment ecosystem. By maintaining a government-backed option, regulators ensure that users have access to a payment system not controlled by foreign corporations or large private companies. This becomes particularly relevant as AI-powered financial services raise questions about data ownership and cross-border data flows.

The success of BHIM and similar initiatives will likely influence future regulatory approaches to digital payments. As India continues to develop its AI capabilities in the financial sector, maintaining options for domestic control over critical payment infrastructure will remain a priority for policymakers and regulators.

Looking ahead

As India's digital payment system approaches its billion-transaction milestone, AI integration represents both tremendous opportunity and significant responsibility. The technology promises to democratize financial services, extend credit to underserved populations, and enhance security across the ecosystem. However, realizing these benefits requires careful attention to regulatory frameworks, data governance, and the development of indigenous capabilities that reflect India's unique needs and values.

The coming years will test whether India's approach to AI-powered payments can serve as a model for other developing economies seeking to leapfrog traditional financial infrastructure. With continued collaboration between NPCI, regulatory bodies, banks, and fintech companies, the vision of an AI-enhanced, universally accessible payment system may prove achievable. The success of initiatives like FIMI demonstrates that focused, well-executed AI applications can deliver immediate value while laying groundwork for more ambitious implementations.

For investors and technology observers worldwide, India's digital payment evolution offers insights into how AI can be deployed at scale in complex, diverse environments. The lessons learned from UPI's growth trajectory and AI integration efforts may influence payment system design in other markets facing similar challenges of scale, diversity, and rapid technological change.