Why this course?

Description

The adoption of Artificial Intelligence, Machine Learning, and automated decision systems across the Indian financial sector has fundamentally changed how institutions assess credit risk, price products, detect fraud, manage capital, and support strategic decisions. As these models become embedded in critical business and regulatory functions, the risks arising from model errors, biased outputs, opaque decision-making, inadequate governance, and unmanaged vendor dependencies have emerged as urgent supervisory concerns.

In August 2025, the Reserve Bank of India released the Framework for Responsible and Ethical Enablement of Artificial Intelligence (FREE-AI), establishing five governance pillars for regulated entities: Accountability, Transparency, Fairness, Data Privacy, and Reliability.

In June 2026, RBI followed with a draft Guidance on Regulatory Principles for Model Risk Management, open for public comment until 24 July 2026. This draft, for the first time, formally extends model governance expectations to every AI and machine learning model used by a regulated entity, including models built or supplied by third-party vendors.

The draft is applicable to Commercial Banks, Small Finance Banks, Payments Banks, Local Area Banks, Regional Rural Banks, Urban and Rural Co-operative Banks, All India Financial Institutions, Non-Banking Financial Companies, Asset Reconstruction Companies, and Credit Information Companies.

Five specific mandates stand out:

A documented mechanism to override or deactivate any AI model (kill switch). Documented human oversight built into AI-driven decisions. Customer disclosure where AI materially influences a decision. Active governance extended to third-party AI vendors, not only in-house models. Board-level accountability for AI governance, with structured reporting separate from general IT risk.

These are no longer aspirational principles. They are becoming enforceable supervisory expectations. Institutions that build this capability before the final circular is issued will be the ones answering supervisory questions with evidence, not scrambling to produce it after the fact.

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Objective

The Risk Management Association of India (RMAI), through its eLearning platform Smart Online Course, is offering a structured, certification-backed program to equip risk, compliance, internal audit, and governance professionals with the frameworks, tools, and practical competencies needed to implement AI model risk management aligned to RBI's evolving expectations and globally recognised standards.

This program comprises two complementary courses that together cover both the governance framework and the operational risk landscape of AI in financial services.

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Program Structure

Course 1: Responsible AI Risk Management using NIST AI Framework
Duration: 9 Hours | Mode: Self-paced Online | Access: 120 Days

Modules:

• Introduction to Responsible AI and NIST AI RMF
• Core Structure of NIST AI RMF
• MAP Function: Risk Context and AI Use Understanding
• MEASURE Function: Assessing and Quantifying AI Risk
• MANAGE Function: Mitigating and Responding to AI Risks
• GOVERN Function: Oversight, Policy and Global Standards
• Industry Use Cases of NIST AI RMF
• Toolkits, Glossary and Downloadable Resources

Course 2: Risk Management for Artificial Intelligence
Duration: 8 Hours | Mode: Self-paced Online | Access: 120 Days

Modules:

• Understanding AI Risks in Organisations
• Global AI Regulations and Standards
• Bias, Fairness and Explainability
• Data Security, Privacy and Piracy Threats
• Adversarial and Systemic AI Risks
• AI in BFSI and Insurance: Risks and Controls
• AI Governance, Audit and Best Practices
• Future Challenges and Failures in AI