INDIA Trends and Developments Contributed by: Shilpa Mankar Ahluwalia, Purva Anand and Ansh Jain, Shardul Amarchand Mangaldas & Co
banks and non-banking financial companies – are using or developing AI systems. Common use cases include customer support, credit underwriting, sales and marketing, and cybersecurity. Interest in genera - tive AI is high, but most uses are still experimental. Firms are cautious with customer-facing deployments because of concerns about data sensitivity, explain - ability and bias. The FREE-AI framework rests on seven foundational
The committee noted that the current legal framework, including the Information Technology Act, 2000 and existing RBI guidelines, is sufficient for present risks. It still recommends AI-specific enhancements to several RBI directions, including on outsourcing, cybersecu - rity, digital lending, customer service and fraud risk management. It also proposes a graded liability and supervisory approach, with a “tolerant supervisory stance” for first-time AI errors where firms have solid safety measures. Conclusion The developments above show a consistent regulatory approach: embed innovation within existing legal and institutional frameworks while building transparent, accountable and supervised infrastructure. Tokenisa - tion has moved from pilot to planned scale-up, with wholesale CBDC as the settlement backbone. Regu - latory consolidation has made compliance easier to navigate, and SROs have created structured channels for industry engagement. The FREE-AI framework sets guardrails for AI that balance innovation with consumer protection and systemic stability. For market participants, the practical next step is to engage early. Tokenisation and CBDC will reshape settlement and market infrastructure over the com - ing years. The consolidated framework and SRO structure open the door to constructive dialogue on implementation. Furthermore, the AI framework sig - nals that institutions should build governance, audit and disclosure capabilities now rather than wait for prescriptive rules. The direction is clear: sustainable, compliant growth backed by infrastructure designed for accountability.
principles (the “Sutras”): • trust as the foundation; • people first; • innovation over restraint; • fairness and equity; • accountability; • understandable by design; and • safety, resilience and sustainability.
These are put into practice through 26 recommenda - tions across six pillars – three to enable innovation (infrastructure, policy and capacity) and three to man - age risk (governance, protection and assurance). On innovation, the framework proposes creating financial-sector data infrastructure as digital public infrastructure to support AI model development. It calls for an AI innovation sandbox for safe experimen - tation, incentives to build indigenous financial-sector AI models, and integration of AI tools with UPI and other digital public platforms. It also stresses capac - ity building within regulated entities and regulators, especially at board and senior management levels. On risk mitigation, the framework recommends that regulated entities adopt board-approved AI policies covering governance, life cycle management, risk controls and third-party liabilities. It requires disclosure when consumers engage with AI systems and allows them to contest AI-driven decisions. It proposes AI- specific enhancements in outsourcing agreements, covering algorithmic bias, data confidentiality and use of AI by third-party vendors and their subcontractors, and sets requirements for AI incident reporting and audit frameworks.
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