New Delhi, Sept 26 India's approach to adopting AI in financial services aligns with the broader goal of leveraging technology for inclusive economic development, and the country’s unique digital public infrastructure lays a foundation for AI integration, aiming to democratise financial access at an unprecedented scale, according to the Finance Ministry's monthly review released on Friday.
The RBI’s FREE-AI Framework is designed to foster innovation while ensuring robust risk management, highlighting that these two elements are complementary forces that should be pursued together. The framework also supports the India AI Mission, enhancing national AI capabilities, and aligns with the Digital Personal Data Protection Act, ensuring consistency in data governance, the review states.
Based on detailed surveys, the RBI found that AI adoption in Indian finance is still in its early stages, with only 21 per cent of surveyed banks and financial institutions implementing or developing AI solutions.
Adoption is concentrated among larger banks, while smaller urban cooperative banks and many NBFCs face resource constraints, including inadequate data infrastructure, limited skilled talent, and insufficient IT budgets, which hinder AI deployment. Additionally, even among early adopters, the use of AI applications remains basic, often focused on improving process efficiency, customer interactions (like simple chatbots), lead generation, and internal decision support rather than engaging in complex autonomous decision-making.
Guided by these principles, the RBI’s FREE AI outlines six strategic pillars for implementing its vision effectively. Under the Innovation Enablement Framework, the focus is on infrastructure, policy, and capacity building. This includes developing shared data and technology infrastructure to democratise AI access (for example, data lakes or plug-and-play ‘landing zones’ platforms that smaller firms can leverage), crafting agile policies and establishing regulatory sandboxes for safe and controlled experimentation, along with addressing the AI skill gap.
The Risk Mitigation Framework focuses on governance, protection, and assurance to create transparent governance structures (like board oversight and ethics committees), implement robust protective measures for privacy and security, and mandate ongoing monitoring and validation of AI systems to ensure reliability, the review added.
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