India is experiencing a significant shift in its financial sector, moving beyond the initial phase of expanding access to digital services toward focusing on quality inclusion, system resilience, and widespread trust. This transition comes as artificial intelligence (AI) becomes increasingly important in shaping the nation’s financial landscape.
A recent roundtable titled “Amrit Niti: AI for Financial Empowerment, Inclusive Growth and Resilient Systems,” organized by Prosus with support from the Ministry of Electronics and Information Technology (MeitY), brought together policymakers, industry leaders, and technology experts. The event aimed to explore how AI could transform India’s financial ecosystem and contribute to a nationwide AI strategy adaptable to other countries in the Global South.
Participants at the roundtable discussed several challenges facing India’s large and complex financial ecosystem. These include more sophisticated fraud patterns, fragmented customer experiences, and rising regulatory expectations. According to Sajeesh Mathew, Member (Economics) at the Pension Fund Regulatory and Development Authority (PFRDA), “AI must remain useful, inclusive, and responsible, with explainability and traceability built into its deployment.” He added that while AI can improve efficiency and risk monitoring, human accountability remains crucial for critical decisions.
Mathew also stressed the importance of developing domestic alternatives for AI tools to reduce reliance on global supply chains. “We need to ensure that AI deployment is secure, cost-effective, and aligned with India’s data sovereignty goals,” he said.
A key issue identified was fragmented data across institutions. Anindya Karmakar from Aditya Birla Capital described their efforts to modernize data platforms for a unified customer view: “AI’s real value emerges when data silos are dismantled,” he said. “By building consent-led architectures and integrating structured and unstructured data, we can deliver more personalized and effective financial solutions.”
Industry-wide frameworks for sharing non-personally identifiable information (non-PII) were also discussed as ways to enhance ecosystem-wide intelligence while maintaining privacy.
Despite advances in digital infrastructure over the past decade that have expanded access to payments systems and formal identities for millions of Indians, many groups—such as MSMEs (micro-, small-, medium-sized enterprises), women-led businesses, and first-time borrowers—still face barriers due largely to rigid risk models or incomplete data. Rajiv Gupta from Policy Bazaar highlighted their efforts in promoting credit awareness: “We’ve provided over 6.5 crore credit score statements free of cost, helping individuals understand and improve their credit scores.” He emphasized that AI could simplify products through local language resources: “AI can help us reach underserved communities by creating video testimonials and local language content, empowering agents and customers alike.”
Hyper-personalization was another theme raised during discussions. Aishwarya Jaishankar from Hyperface said: “AI enables us to deliver hyper-personalized offers and solutions in real time, tailored to individual customer needs and behaviors.” She noted this approach is especially important for groups like farmers or first-time borrowers.
Fraud prevention remains a growing concern as attacks become more coordinated across institutions. Mohit Gopal of PayU explained: “Fraud detection cannot remain siloed. If one institution identifies fraudulent behavior, delays in information flow allow the same activity to surface elsewhere. A shared intelligence layer is essential to address this challenge.”
The impact of AI on employment was also debated; while some jobs may be displaced by automation or new technologies such as deep learning models or natural language processing systems—which require expertise—there are opportunities created around areas like model development or annotation work supporting machine learning projects. Rentala Chandrasekhar from Centre for the Digital Future commented: “AI adoption in India has the potential to play out differently. By addressing unmet needs and creating new services, we can ensure that job creation outpaces displacement.” Vikas Agnihotri underscored pairing adoption with workforce training programs.
Shailesh Paul of Wibmo remarked that organizations should see AI primarily as an assistive tool rather than an autonomous actor: “Responsible deployment requires as much organisational change as technical capability.”
Rentala Chandrasekhar further pointed out issues surrounding underutilized data assets within India due mainly because most datasets are kept isolated within institutional boundaries rather than being shared even voluntarily: “If all the data is lying in silos and there is no way for it to be shared… then its potential remains untapped.”
The roundtable concluded there is broad agreement among stakeholders about embedding AI deliberately within Indian finance—with coordination needed among regulators; traditional banks; fintech companies; policymakers; clear rules on governance/sharing/intelligence frameworks; ongoing investment into workforce skills—and above all adopting a mindset where national capability takes precedence over efficiency alone.
Sehraj Singh from Prosus India summarized these sentiments: “Every major technological transition has been accompanied by uncertainty but also by opportunity… If we align technology with trust skills & policy intent then AI can become force multiplier not just efficiency.”
As choices made now will shape whether divides deepen or close further—the Amrit Niti initiative aims at ensuring intelligent systems strengthen trust expand opportunity build resilience across Indian finance long term.
