RBI’s FREE-AI Committee Report | 16 Aug 2025

Source: TH 

Why in News?

The Reserve Bank of India (RBI) has released the Framework for Responsible and Ethical Enablement of Artificial Intelligence (FREE-AI) Committee Report. It calls for 7 guiding sutras to promote responsible AI use in the financial sector while balancing innovation and risk mitigation. 

What are the RBI’s 7 Sutras for AI adoption Under FREE-AI? 

• Trust is the Foundation: Trust is non-negotiable and should remain uncompromised. Build Artificial Intelligence (AI) systems that are reliable, transparent, and inspire public confidence. 
• People First: AI should support human decision-making but defer to human judgment and citizen interest, prioritising welfare, dignity, and inclusion. 
• Innovation over Restraint: Encourage responsible innovation while avoiding unnecessary restrictions. 
• Fairness and Equity: AI outcomes should be fair and non-discriminatory. 
• Accountability: Accountability rests with the entities deploying AI and clearly define responsibilities for AI decisions and their impacts. 
• Understandable by Design: Make AI systems and their decisions interpretable for users and regulators. 
• Safety, Resilience, and Sustainability: Develop AI that is secure, adaptable, and sustainable in the long term. 

What Can be the Significance of AI in Finance? 

• Revenue Growth: AI is projected to drive significant revenue growth, with investments in financial services expected to reach Rs 8 lakh crore by 2027. 
• Efficiency and Personalization: AI can streamline repetitive and time-consuming tasks, enabling financial institutions to process large volumes of data more quickly and accurately, such as in loan application processing. 
• Financial Inclusion: AI uses alternative data (like utility bills, GST filings) to assess creditworthiness, enabling loans to “thin-file or new borrowers” excluded from traditional systems. 
• Innovation in Digital Infrastructure: AI enhances India's digital public infrastructure (e.g., Aadhaar, Unified Payments Interface) to provide personalized, adaptive financial services. 
• Better Risk Management: AI helps in fraud detection, early risk warnings, and improved decision-making, optimizing risk management processes. 
  • J.P. Morgan’s AI payment validation cut fraud, lowering account rejection rates by 15–20%. 
• Synergies with Emerging Tech: AI's integration with quantum computing and privacy technologies promises enhanced performance and security in finance. 

What are the Challenges of AI in Finance? 

• Model Bias and Risk: AI models can inherit biases from training data, leading to unfair decisions. The "black box" nature makes them hard to audit. 
• Third-Party Risks: Heavy reliance on a few vendors or cloud providers can cause service disruptions, software issues, and cyber vulnerabilities. 
• Regulatory and Liability Concerns: The lack of transparency in AI models complicates liability allocation in case of errors or biased outcomes. 
• Cybersecurity Threats: While AI can improve security, it also opens new attack routes like data poisoning, adversarial inputs, and deepfakes. 
• Ethical and Consumer Protection Issues: Algorithmic bias, privacy violations, and lack of transparency can undermine consumer trust and exclude vulnerable groups. 
• Risk of Non-Adoption: Not using AI can hurt competitiveness, reduce efficiency, slow financial inclusion, and leave systems exposed to AI-powered threats. 

What are the RBI’s Recommendations for AI in Finance? 

• Innovation Enablement: Establish high-quality financial sector data infrastructure as part of digital public infrastructure, integrated with AI Kosh. 
  • Create an AI Innovation Sandbox like GenAI Digital Sandbox, a secure test environment for financial institutions to trial AI models using anonymised data, with tools to detect bias or errors and ensure compliance with AML, KYC, and consumer protection norms. 
• Consumer Protection & Security:  Organizations should conduct proportionate AI red teaming through periodic and trigger-based tests and implement incident reporting frameworks with good-faith disclosure to manage AI risks effectively. 
• Capacity Building within REs: Develop structured training programs for AI governance and risk mitigation at all levels within institutions. 
  • Establish frameworks for exchanging AI use cases and best practices across the financial sector. 
• AI Incident Reporting: Create an AI incident reporting framework for timely detection and disclosure of AI-related issues. 

Conclusion 

The FREE-AI framework outlines how AI can be responsibly and ethically adopted in India’s financial sector, offering a roadmap for regulators, financial institutions, and technology providers to harness AI’s potential effectively.