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Reserve Bank of India Proposes Risk Management Framework For AI Models
Reserve Bank of India Proposes Risk Management Framework For AI Models

Reserve Bank of India Proposes Risk Management Framework For AI Models

The Reserve Bank of India proposes a risk management framework for AI models within institutions of finance and banking.

Published Updated

Key takeaways

  • The Reserve Bank of India has proposed a risk management framework specifically for AI models in financial institutions.
  • Inadequate management of model risks can result in poor decisions, financial losses, and compliance issues.
  • The draft applies to a wide range of financial entities, including banks and non-banking financial companies.
  • Models must undergo independent validation before deployment and periodically thereafter.
  • Entities are required to establish explainability thresholds for AI models impacting significant decisions.
5 min

The Reserve Bank of India has introduced a draft for the management of AI models. The Economic Times noted that regulated entities are increasingly relying on models for business processes, customer services, risk management and cyber defence. The authority warns that inadequate management of model risks could lead to flawed decisions, financial losses, operational disruptions and compliance failures.

The draft ‘Guidance on Regulatory Principles for Model Risk Management, 2026’ applies to commercial banks, small finance banks, payments banks, cooperative banks, NBFCs, all-India financial institutions, asset reconstruction companies and credit information companies. The framework mandates models “must be covered under a board-approved Model Risk Management Framework”, according to reports. 

The models must be independently validated before and after deployment, following modifications and at periodic intervals. Models require approval from the risk committee.

The draft proposes safeguards for AI systems. The draft requires entities to define explainability thresholds for AI models and apply higher standards where model outputs influence material decisions or have significant customer impact. When systems lack explainability, the text states entities must implement enhanced validation, monitoring, output verification and usage restrictions.

The framework introduces rules for AI systems that face customers. The draft states entities must inform users that they are interacting with an AI-based system and provide an option to switch to human assistance on request. The authority expects institutions to establish robust human oversight mechanisms for AI models and to include arrangements to override, suspend or deactivate models, including kill-switch arrangements.

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