Understanding RBI's AI Governance Framework for Indian Banks

The Reserve Bank of India has been progressively articulating expectations around AI governance for regulated entities. While there isn’t yet a single comprehensive AI regulation, various circulars and guidelines provide clear direction on what RBI expects from banks deploying AI systems.

Key Areas of Focus

Model Risk Management

RBI expects banks to have robust model risk management frameworks, particularly for AI/ML models used in credit decisioning, fraud detection, and customer-facing applications. This includes:

Model validation and testing protocols
Ongoing monitoring for model drift
Clear documentation of model assumptions and limitations
Regular model performance reviews

Data Governance

Data quality and governance are fundamental to AI reliability. RBI emphasizes:

Data lineage and provenance tracking
Data quality metrics and monitoring
Clear data ownership and accountability
Compliance with data localisation requirements

Explainability and Transparency

For customer-impacting decisions, RBI expects banks to be able to explain AI decisions in terms customers can understand. This is particularly critical for:

Credit approval and rejection
Fraud alerts and account blocks
Personalized pricing and offers

Implementation Approach

Banks should adopt a phased approach to AI governance:

Assessment: Inventory all AI/ML models and classify by risk
Framework: Establish governance policies and procedures
Technology: Implement monitoring and control infrastructure
Culture: Train staff and embed governance in development processes

How Rotavision Helps

Our Guardian platform provides the monitoring infrastructure banks need to meet RBI expectations. With 96% detection accuracy for AI reliability issues, banks can identify problems before they impact customers or attract regulatory scrutiny.