RBI FREE-AI Framework: Seven Sutras, Six Pillars for Responsible AI in Finance
RBI's FREE-AI sets a blueprint to scale AI with trust, fairness, and accountability. Leaders must tighten vendor contracts, formalize AI policy, and build team capacity now.
RBI issues FREE-AI framework: what finance leaders need to do now
On 13 August 2025, the Reserve Bank of India released the Framework for Responsible and Ethical Enablement of Artificial Intelligence (FREE-AI) - a clear blueprint for deploying AI across India's financial system. A high-level committee chaired by Professor Pushpak Bhattacharya of IIT Bombay shaped the framework with a dual focus: enable AI at scale and control risk with discipline.
The document emphasizes digital public infrastructure, indigenous model development, adaptive policy, and AI innovation sandboxes. It also zeroes in on capacity building across institutions and supervisors, making people and process readiness as critical as technology.
The seven sutras (guiding principles)
- Trust
- People first
- Innovation over restraint
- Fairness and equity
- Accountability
- Understandable by design
- Safety, resilience and sustainability
These principles support 26 recommendations grouped under six pillars: infrastructure, policy, capacity, governance, protection and assurance.
What FREE-AI means for your AI agenda
- Infrastructure: Build on DPI, strengthen data quality, and prepare for model monitoring and fallback paths.
- Policy: Set adaptive policies that evolve with model capability, use, and risk.
- Capacity: Upskill risk, compliance, audit and frontline teams to work with AI safely and effectively.
- Governance: Define ownership, decision rights, escalation routes, and model documentation standards.
- Protection: Embed consumer disclosures, consent, recourse, and fairness testing into every AI touchpoint.
- Assurance: Plan independent audits, stress tests, and business continuity for AI-dependent processes.
Risk controls to implement now
- Board-approved AI policy: Cover lifecycle (design to retirement), model risk appetite, explainability thresholds, data provenance, and human-in-the-loop checkpoints.
- AI-specific consumer protection: Plain-language disclosures when AI is used, complaint pathways, and bias monitoring for credit, claims, pricing, and collections.
- Audit mechanisms: Independent reviews of training data lineage, feature relevance, drift, bias, and performance by segment; periodic red-teaming for fraud and abuse.
- Business continuity: Tested fallback operations for model failure or third-party outages, roll-back plans, and manual override protocols.
- Third-party controls: Apply RBI outsourcing guidelines to AI vendors with contracts that specify bias testing duties, accountability, data-use limits, IP ownership, security, audit rights, incident reporting, model change notifications, and data localization where applicable.
Third-party AI: tighten your contracts
FREE-AI flags growing reliance on external AI providers and clarifies the use of RBI's outsourcing rules for these relationships. Contracts should explicitly address fairness metrics, explainability deliverables, model versioning, data retention and deletion, and clear liability for adverse outcomes.
Where this fits globally
FREE-AI is consistent with international thinking on trustworthy AI and system resilience. It mirrors elements from the OECD AI Principles and the Bank of England's focus on oversight and resilience.
Who benefits and how
- MSMEs: Fairer credit access with explainable models and data-light underwriting; better discovery in digital marketplaces.
- RegTechs: Clearer guardrails that complement industry codes, easing onboarding with banks and insurers.
- Banks, NBFCs, insurers, markets, payments: Safer deployment of AI for customer engagement, automation, and fraud detection with clearer accountability.
The market trajectory
Investments in AI across banking, insurance, capital markets and payments are projected to exceed INR 8 trillion (USD 97 billion) by 2027. Generative AI alone is expected to cross INR 1.02 trillion by 2033, at 28%-34% CAGR, as firms scale use across service, operations, and risk.
90-day execution checklist
- Appoint an accountable executive (one throat to choke) and form a cross-functional AI risk working group.
- Inventory all AI use cases and third-party dependencies; classify by risk and customer impact.
- Draft and seek board approval for an AI policy covering lifecycle, metrics, and controls.
- Define fairness metrics and testing cadence for credit, pricing, claims, and collections.
- Update procurement templates with AI clauses (bias, data use, audit rights, incident reporting, model change control).
- Stand up documentation standards: model cards, data sheets, lineage, and decision logs.
- Run a low-risk pilot in an innovation sandbox with pre-set success and safety criteria.
- Train risk, compliance, audit, and frontline teams on AI basics, oversight, and incident response. For role-based learning, see AI upskilling for finance roles.
Key takeaways for leadership
- Balance speed with guardrails: Innovation sandboxes and adaptive policies let you ship safely.
- Make accountability explicit: Name owners for models, data, decisions, and incidents.
- Treat vendors as extensions of your risk posture: Contracts and monitoring must match internal standards.
- Invest in people: Capability building is as critical as models and infrastructure.
FREE-AI gives India's financial sector a clear path: adopt AI that is safe, fair, and understandable - at scale. The sooner you operationalize these controls, the faster you can capture value with confidence.
