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16 articles on digital banking, fintech innovation, and AI in financial services.
In Sriperumbudur and Hosur, the factory floors tell a new story. What once produced wires and plastic for local markets now ships iPhones to the world. Apple exported USD 25 billion worth of iPhones in the first half of FY25, and India is on track to hit USD 50 billion by year-end — almost 4% of the MSME economy. Beyond Foxconn and Tata, hundreds of smaller suppliers are scaling up with Apple, employing 350,000 Indians today and potentially 1 million by 2030. This manufacturing boom is also a finance story. Vendor credit, export finance, insurance, EMIs, and employee loans together could unlock a ₹7 trillion BFSI opportunity by 2030. It’s a replay of the Maruti-Suzuki effect in the 1980s — when auto suppliers grew into global giants. Now, Apple is catalysing the same transformation in electronics, but at global scale and speed. Every iPhone exported isn’t just a product. It’s credit, insurance, IPOs, and housing loans in motion. An Apple a day doesn’t just make India happy — it makes Indian BFSI very happy.
Artificial Intelligence promises to make banking faster, smarter, and more consistent—catching fraud, improving credit decisions, and tightening compliance. Yet, its rise complicates the very notion of accountability. Just as boards and regulators already struggle to assign responsibility in complex financial systems, AI introduces another opaque layer where outcomes may be explainable to no one. The danger lies in treating AI as a “hired gun” to blame, while the real solution may require doubling down: AI monitoring AI, embedding it into control frameworks, and redefining accountability architectures altogether. The future of banking governance won’t be about humans versus machines—it will be about building systems where both are inseparably responsible.
AI governance in India’s BFSI sector stands at a critical inflection point. While the RBI’s FREE-AI Report lays a solid foundation with its seven sutras—fairness, transparency, accountability, safety, inclusivity, human oversight, and sustainability—the real challenge lies in translating frameworks into practice. As the sector grapples with balancing innovation and regulation, five key imperatives emerge: regulate outcomes, not technologies; establish guardrails for new AI-enabled activities; leverage AI as a leapfrog moment through regulator-led sandboxes; use AI to strengthen ethics, not just efficiency; and build indigenous AI ethics rooted in Indian values. If executed well, India’s approach could transform governance from a compliance exercise into an instrument of trust—and position the country as a global standard-setter in AI-led financial services.
AI won’t eliminate accountability in banking—it will complicate it. While AI brings speed, scale, and consistency, it also blurs the lines of responsibility. When decisions come from systems no one fully understands, blaming “the algorithm” becomes dangerously easy. True accountability in this new era means building AI not just to decide—but to oversee, audit, and predict failure. We can’t go back to human-only oversight. But we also can’t hide behind AI. The future of banking demands new control systems—and a redefined architecture of trust.
The RBI’s 2025 draft directive on Digital Banking Channels Authorisation is a major regulatory reset, consolidating 16 fragmented circulars into one cohesive framework. It brings much-needed clarity, ensuring uniform compliance across all banks—including RRBs and cooperative banks—while strengthening customer protections with features like explicit consent and read-only modes. Yet, while it simplifies governance, the draft risks slowing innovation by banning third-party promotions on digital platforms, potentially hampering ONDC integrations and embedded finance. Smaller banks may also struggle with the uniform compliance load. The underused Digital Banking Units (DBUs) offer a chance to reinvent legacy banking—if granted operational freedom. Now, with regulation streamlined, the next step is for banks to treat digital not just as a service channel, but as a strategic business driver.
JPMorgan Chase, the titan of U.S. consumer and SME banking, is making waves by charging third-party fintechs—like Plaid—hundreds of millions to access customer data via APIs. On the surface, it appears to be a strategic monetization move. But dig deeper, and it's clear: this isn’t about revenue. With API fees generating less than 0.5% of JPMorgan’s annual income, the goal is control, not cash. By raising access costs, the bank is effectively tightening its grip on the fintech ecosystem—potentially pricing out smaller innovators and stifling the open-data-driven progress that has defined modern finance. This quiet power shift raises a critical question: who truly owns financial data—the bank or the customer? In contrast to countries like India, where open banking policy puts data control in the hands of the user, JPMorgan’s approach could signal a retreat from openness. If not tempered with thoughtful safeguards, such a move risks protecting the core while starving future innovation. The battle for the future of finance may not be over infrastructure, but over who chooses to build bridges—or walls.
In a candid and insightful narrative, the author challenges the myth that innovation in BFSI (Banking, Financial Services, and Insurance) is stagnant beyond visible consumer tech like ATMs and UPI. Drawing on decades of global banking and fintech experience, the piece dives deep into the hidden layers of financial innovation—from risk engineering and capital structuring to distribution and access. With examples spanning global markets, from India’s UPI and Account Aggregators to DeFi’s smart contract-powered lending, the article argues that the real transformation in finance is subtle, powerful, and happening beneath the surface.
In fintech, compliance is often seen as a hurdle—but what if it's actually the blueprint? This article unpacks how regulation in the BFSI sector is not a barrier to innovation but the operating system it runs on. By decoding regulatory intent—from customer protection to market integrity—and embracing sandboxes, firms can align ambition with accountability. As AI reshapes decision-making in finance, the need for explainability, auditability, and human oversight is more critical than ever. The takeaway? Regulation isn’t a bug—it’s a feature. Learn to build with it, not around it.
Outdated software isn’t just inefficient—it’s dangerous. From nuclear systems running on floppy disks to billion-dollar frauds hidden in legacy databases, the risks of relying on antiquated tech are growing. This blog unpacks why banking and government institutions still cling to legacy systems, and how AI-led modernization is no longer a luxury but a necessity for survival and competitive edge.
In May 2025, the RBI made a bold regulatory shift by disallowing NBFCs from offsetting loan provisions using Default Loss Guarantees (DLGs) — even when backed by cash. While this may seem like penalizing prudent risk-sharing between fintechs and lenders, it underscores a deeper philosophy: true resilience lies in self-reliance. The RBI’s move signals that provisioning isn’t about probable recoveries but about capital readiness. It’s a call for NBFCs to carry their own balance sheet burdens — without leaning on external assurances — and a reminder that guarantees, no matter how secure, are not substitutes for accountability.
The establishment of tech arms within banks is not a novel concept but has gained significant relevance in today's landscape. Various banks, such as SBI and IDBI, have ventured into this space, generating substantial revenues. However, the focus has primarily been on foundational technology rather than innovation. As India faces a technological upheaval in the BFSI sector, the integration of IT expertise is crucial. With a workforce of 5.4 million in Indian IT, and the ongoing transition among major IT players, there's an opportunity for banks to leverage this talent.
Akhil articulates the risks posed by AI-generated data to the BFSI sector, likening it to toxic sludge that undermines data integrity. The evolution from hard to soft collateral, driven by exponential data growth, has led to initiatives like OCEN and GST-Sahay, establishing 'data-collateral' in lending decisions. However, the rise of AI-generated data threatens to corrupt these systems, as evidenced by the emergence of services like Pearl that promote human-verified results. To combat this, a three-pronged strategy is proposed: fortified source-system authentication, recalibrating social signals, and implementing transaction intelligence.