The RBI’s Framework for Responsible and Ethical Enablement of Artificial Intelligence (FREE-AI) committee, set up in December 2024, has proposed 26 recommendations under six pillars to promote ethical AI use in finance.

About the FREE-AI Committee

• Constituted by: Reserve Bank of India (RBI).
• Chairperson: Dr. Pushpak Bhattacharyya.
• Mandate: Study the present level of AI adoption in financial services and review regulatory and supervisory approaches on AI with a focus on the financial sector globally.
• Purpose: Develop a framework for responsible and ethical AI use in the financial sector.
• Focus: Ensure consumer protection while enabling AI adoption.
• Terms of Reference:
  • Assess AI adoption in financial services in India and globally.
  • Review global regulatory and supervisory approaches to AI in finance.
  • Identify AI-related risks and recommend frameworks for evaluation, mitigation, and monitoring, including compliance for banks, NBFCs, FinTechs, and PSOs.
  • Recommend a governance framework for ethical AI adoption in India’s financial sector.
  • Address any other AI-related matters in the financial sector.

Benefits and Opportunities of AI in Financial Services

• Operational & Business Advantages:
  • Enhances efficiency, accuracy, and personalisation at scale.
  • For Example  AI analytics for customer behaviour and risk management.
• Financial Inclusion: 
  • Assess creditworthiness using non-traditional data (utility bills, mobile usage, GST filings).
  • Voice-enabled banking in regional languages for illiterate/semi-literate users.
  • Chatbots for financial literacy, grievance redressal, and behavioural nudges.

The Seven Sutras – Guiding Principles

The Committee has formulated 7 Sutras – a set of foundational principles that will guide the development, deployment, and governance of AI in the financial sector.

• Trust is the Foundation: Trust is non-negotiable and should remain uncompromised
• People First: AI should augment human decision-making but defer to human judgment and citizen interest
• Innovation over Restraint: Foster responsible innovation with purpose
• Fairness and Equity: AI outcomes should be fair and non-discriminatory
• Accountability: Accountability rests with the entities deploying AI
• Understandable by Design: Ensure explainability for trust
• Safety, Resilience, and Sustainability: AI systems should be secure, resilient and energy efficient

FREE-AI Framework

The FREE-AI Framework translates the 7 sutras into actionable recommendations through two complementary sub-frameworks

1. Innovation Enablement Framework

• Focus: Unlock AI’s transformative potential while ensuring responsible adoption.
• Pillars:
• • Infrastructure – Build data ecosystems, compute capacity, and public goods to support AI development.
  • Policy – Create adaptive, agile regulatory frameworks to encourage innovation.
  • Capacity – Strengthen human skills and institutional ability to manage AI responsibly.

2. Risk Mitigation Framework

• Focus: Address risks from AI integration in finance.
• Pillars:
• • Governance – Establish clear structures for oversight of AI decisions and actions.
  • Protection – Safeguard consumers and systems from harm.
  • Assurance – Ensure ongoing validation, monitoring, and control of AI systems.

Key Recommendations

• Data Infrastructure: Establish a high-quality financial sector data infrastructure as digital public infrastructure, integrated with AI Kosh – India Datasets Platform under the IndiaAI Mission.

• Sandbox: Set up an AI Innovation Sandbox to test AI solutions in a controlled environment.
• Indigenous AI Models: Develop financial sector-specific AI models to reduce dependence on foreign technologies.
• Adaptive AI Policy: Formulate enabling AI policies that evolve with technological advancements.
• AI Liability Framework: Introduce clear accountability rules for AI-related outcomes.
• Executive and Regulatory Training: Build AI-related capacity for boards, C-suite executives, regulators, and supervisors.
• Institutional Capacity Building: Conduct training and strengthen regulatory oversight and technical competence.
• Risk-Based AI Audits: Implement a board-approved AI audit framework based on AI risk categorisation.
• Lifecycle Audit Coverage: Ensure audits assess data inputs, algorithms, and decision outputs.
• Cybersecurity and Consumer Protection: Enhance safeguards for AI-powered financial services.

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