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A Professional Programme Covering RBI’s 2026 Framework, Model Validation and AI/ML Risks
17-Hour Specialised Learning Programme | 10 Modules | 52 Lectures | Global Case Studies
The Risk Management Association of India (RMAI) is introducing a specialised professional learning programme, Model Risk Management in Banking & Financial Institutions, designed to strengthen the capabilities of banking, financial services and risk management professionals in governing, validating, monitoring and managing the risks associated with quantitative, statistical and AI-enabled models.
The programme has been developed against the backdrop of significant regulatory developments in India and internationally, particularly the Reserve Bank of India’s June 2026 draft Guidance on Regulatory Principles for Model Risk Management.
Through 10 structured modules and 52 lectures, the course addresses the complete model lifecycle, from identification and risk classification to development, independent validation, monitoring, third-party oversight, AI/ML governance and enterprise-level risk reporting.
With 17 hours of structured learning, the programme integrates Indian regulatory developments, international supervisory frameworks, practical examples and 52 case studies, industry illustrations and regulatory examples.
1. Why Model Risk Management Has Become a Critical Banking Capability
Banks and financial institutions increasingly depend on models to support decisions involving credit underwriting, pricing, provisioning, capital assessment, liquidity management, fraud detection and financial crime monitoring.
A credit-scoring model may determine a borrower’s eligibility for financing. An Expected Credit Loss model may influence impairment provisions. Market-risk models may support trading limits and capital decisions. AI-enabled fraud detection systems may determine which transactions require further investigation.
However, these models depend on data, assumptions, methodologies and implementation processes that may be incomplete, inaccurate or unsuitable for changing conditions.
A model can produce unreliable results because of defective data, unrealistic assumptions, programming errors, inadequate validation, inappropriate use or deterioration after deployment.
The consequences may extend beyond inaccurate predictions. Model weaknesses can affect capital adequacy, lending decisions, financial reporting, customer outcomes, compliance and institutional reputation.
Model Risk Management (MRM) provides the governance and control framework needed to understand these limitations, challenge model outputs and ensure that models remain appropriate for their intended purposes.
As institutions increase their reliance on advanced analytics and artificial intelligence, model risk is becoming an enterprise-wide management responsibility rather than a concern limited to quantitative modelling teams.
2. RBI’s 2026 Draft Model Risk Management Guidance: A Major Regulatory Development
On 24 June 2026, the Reserve Bank of India released its draft Guidance on Regulatory Principles for Model Risk Management for public consultation.
The draft builds on RBI’s earlier proposal of 5 August 2024 concerning model risks in credit.
While the 2024 proposal focused primarily on credit-related models, the June 2026 draft takes a broader approach by addressing risks associated with models used across relevant business and decision-making processes.
The proposed framework covers 11 categories of RBI-regulated entities, including commercial banks, Small Finance Banks, Payments Banks, Regional Rural Banks, co-operative banks, All India Financial Institutions, NBFCs, Asset Reconstruction Companies and Credit Information Companies.
It extends to internally developed models, externally sourced models and models employing Artificial Intelligence or Machine Learning.
Major Areas Covered by the Draft Framework
Board-level governance: A comprehensive model-risk framework supported by clear management responsibilities, risk appetite and oversight.
Model identification and inventory: Processes to identify models and maintain information about their ownership, purpose, risk classification, dependencies and governance status.
Risk-based model tiering: Differentiated oversight based on materiality, complexity, criticality and potential consequences of model failure.
Independent validation: Effective challenge of methodology, assumptions, data quality, implementation and model performance.
Lifecycle controls: Governance covering model development, deployment, ongoing monitoring, changes, recalibration and retirement.
Third-party accountability: Appropriate due diligence, validation and oversight of externally developed and vendor-provided models.
AI/ML model risk: Additional considerations involving explainability, data representativeness, bias, robustness, human oversight and changing model behaviour.
These developments highlight a growing need for professionals who can translate model-risk principles into operational governance and practical risk management processes.
Regulatory status: The RBI document issued on 24 June 2026 was a draft for consultation, with comments invited until 24 July 2026. The proposals should not be described as binding requirements unless and until finalised by RBI.
3. International Regulatory Developments Reinforce the Importance of Model Risk Management
India’s regulatory developments are part of a wider international emphasis on model governance.
United States: Revised Model Risk Management Guidance, April 2026
On 17 April 2026, the Federal Reserve, Office of the Comptroller of the Currency and Federal Deposit Insurance Corporation issued revised interagency Model Risk Management guidance.
The revised framework superseded the earlier SR 11-7 guidance and emphasised a risk-based, proportionate approach.
It addresses model development and use, validation and monitoring, governance and controls, and third-party model products.
The revised US guidance excludes Generative AI and Agentic AI models from its specific scope, making it important for institutions to distinguish conventional MRM expectations from broader AI governance requirements.
United Kingdom: PRA Model Risk Management Principles
The Prudential Regulation Authority’s SS1/23, updated on 23 April 2026, establishes five principles covering model identification, governance, development, independent validation and model-risk mitigation.
It recognises Model Risk Management as a distinct discipline requiring appropriate senior management accountability.
Canada: Enterprise-Wide Model Risk Management
Canada’s Office of the Superintendent of Financial Institutions issued its revised Guideline E-23 on 11 September 2025, with an effective date of 1 May 2027.
The framework promotes enterprise-wide, risk-based Model Risk Management and explicitly considers models employing AI/ML techniques.
Together, these developments demonstrate the increasing supervisory emphasis on model reliability, independent challenge, lifecycle controls and institutional accountability.
4. About RMAI’s New Model Risk Management Programme
The Model Risk Management in Banking & Financial Institutions programme has been structured to provide professionals with both a regulatory understanding and a practical appreciation of Model Risk Management.
Rather than focusing exclusively on mathematical modelling techniques, the programme examines how institutions should govern models, identify and assess model risks, validate model outputs, monitor performance and manage risks arising from changing conditions.
It covers traditional statistical models alongside AI/ML systems and externally developed models.
The curriculum follows a complete model-risk lifecycle, allowing participants to understand how individual controls connect to enterprise-wide risk management responsibilities.
Programme Structure
| Programme component | Coverage |
|---|---|
| Total Duration | 17 Hours |
| Modules | 10 Specialised Modules |
| Lectures | 52 Structured Lectures |
| Examples and Case Material | 52 Distinct Case Studies, Regulatory and Industry Examples |
| Regulatory Coverage | RBI, Basel, US Federal Banking Agencies, UK PRA, EBA and OSFI |
| Application Areas | Credit, ECL, Market Risk, Liquidity, ALM, Fraud, AML, Capital Planning and AI/ML |
5. Detailed Course Curriculum: Ten Specialised Modules
Module 1: Foundations of Model Risk Management
The opening module develops an understanding of what constitutes a model, how models differ from rules and calculations, and why they are important across banking and financial institutions.
Participants examine model risks arising from assumptions, inadequate data, methodology limitations, implementation errors and inappropriate use. The module also distinguishes model risk from operational risk and model performance risk.
Illustrative case studies: Goldman Sachs Apple Card algorithm investigation, AIG Financial Products, Moody’s CPDO coding error and JPMorgan Chase’s London Whale incident.
Learning outcome: Recognise major sources of model risk and understand why MRM is a separate risk management discipline.
Module 2: Regulatory Framework and MRM Governance
This module examines RBI’s evolving regulatory approach alongside international supervisory frameworks.
Participants study the 2024 RBI credit model-risk proposal, the broader 2026 draft guidance and relevant US, UK, European and Canadian approaches.
The governance component covers the responsibilities of the Board, senior management, Model Risk Committee, model owners, developers, validators, users and Internal Audit. Particular attention is given to independence, accountability and the Three Lines Model.
Learning outcome: Understand regulatory expectations and establish clear governance responsibilities for Model Risk Management.
Module 3: Model Identification, Inventory and Risk Tiering
Financial institutions may use large numbers of models across different business functions, including models embedded in technology platforms and vendor products.
This module explains how to identify models, maintain an enterprise model inventory and assess materiality, complexity and inherent risk.
Participants examine model ownership, documentation, dependencies, status, approved uses and validation information. Risk-based tiering is introduced to determine appropriate levels of validation, approval, monitoring and reporting.
Learning outcome: Develop the ability to establish model inventories and apply proportionate governance according to model risk.
Module 4: Model Development, Data, Methodology and Implementation
This module examines the risks arising during model design, development and implementation.
Topics include data selection, accuracy, completeness, representativeness, lineage, variable selection, statistical assumptions and parameter estimation. Participants also examine overfitting, underfitting, sensitivity testing, coding errors, production implementation and documentation requirements.
Illustrative cases: Equifax credit data coding errors, Long-Term Capital Management and AXA Rosenberg’s quantitative model coding incident.
Learning outcome: Identify weaknesses in model methodology, data, development processes and production implementation.
Module 5: Independent Model Validation and Model Testing
The programme’s most extensive module focuses on independent validation and effective model challenge.
Participants examine conceptual soundness, data and implementation validation, outcomes analysis and quantitative model testing.
Coverage includes:
- Back-testing and predicted-versus-actual analysis.
- Sensitivity and stress testing.
- Benchmarking and challenger models.
- Validation independence and competence.
- Model limitations, approval conditions and corrective actions.
The module also examines the documentation and governance of validation findings.
Learning outcome: Understand how to assess model reliability, challenge assumptions and determine whether models are suitable for their intended use.
Module 6: Model Monitoring, Drift, Overrides and Change Management
A model that performs adequately when introduced may become unreliable as economic conditions, customer behaviour and input data change.
This module explains ongoing performance monitoring, data drift, population drift and concept drift.
Participants also examine management overlays, model overrides, recalibration, redevelopment, change approval, revalidation and model retirement.
Illustrative applications: IFRS 9 model adjustments during COVID-19, credit-model recalibration and model replacement during the global LIBOR transition.
Learning outcome: Understand how institutions detect model deterioration and govern model changes throughout the post-deployment lifecycle.
Module 7: Model Risk Across Major Banking Functions
This module connects Model Risk Management principles to major banking and financial applications.
Participants examine model-risk considerations involving:
- Credit scoring, Probability of Default, Loss Given Default and Exposure at Default.
- IFRS 9 and Expected Credit Loss.
- Value at Risk, Expected Shortfall, pricing and valuation.
- Liquidity, Asset Liability Management and interest-rate risk.
- Fraud detection, AML and transaction monitoring.
- Capital planning and stress testing.
Illustrative cases: Silicon Valley Bank, National Australia Bank, Danske Bank and supervisory capital-planning reviews.
Learning outcome: Recognise how model-risk challenges differ across financial functions while applying a consistent enterprise MRM framework.
Module 8: Third-Party, Vendor and External Model Risk
Banks and financial institutions frequently rely on external credit-scoring systems, proprietary algorithms, market-data providers and vendor-developed technology.
This module examines the risks associated with limited model transparency, external data dependencies, inadequate documentation and vendor-driven changes. Participants study model-specific due diligence, independent validation, contractual safeguards, audit rights, concentration risk and exit planning.
Learning outcome: Understand how to manage model risk when models are developed, supplied or operated by third parties.
Module 9: AI/ML Model Risk Management
The growing adoption of Machine Learning and advanced analytics creates additional challenges for Model Risk Management.
This module examines training-data quality, model complexity, explainability, robustness, model drift and human oversight. Participants also consider the implications of foundation models, Generative AI, externally hosted AI applications and changing model behaviour.
The emphasis is on incorporating AI/ML model risk within enterprise MRM, rather than repeating the wider content of dedicated Responsible AI or AI Governance programmes.
Learning outcome: Recognise distinctive AI/ML model-risk issues and understand how conventional MRM principles should be supplemented by appropriate governance controls.
Module 10: Model Risk Reporting, Mitigation and Enterprise MRM
The final module connects individual model issues to enterprise-wide risk governance.
It explains model-risk dashboards, Key Risk Indicators, risk appetite, validation exceptions, outstanding remediation and model-performance deterioration.
Participants examine model-risk mitigation, usage restrictions, compensating controls, escalation and reporting to senior management, Risk Committees and the Board.
Learning outcome: Understand how to aggregate, report and mitigate model risk across the institution and support effective governance decisions.
6. Learning Through Real-World Case Studies
A distinguishing feature of the programme is its allocation of 52 separate case studies, industry examples and regulatory illustrations across the 10 modules.
These include financial-sector incidents involving incorrect assumptions, model implementation errors, inadequate validation and changing market conditions.
Examples from JPMorgan Chase, Moody’s, AXA Rosenberg, Silicon Valley Bank and other financial institutions demonstrate how model weaknesses can affect risk measurement, operational decisions, financial outcomes and governance.
The curriculum also uses regulatory examples from the RBI, US federal banking agencies, PRA, ECB, EBA and OSFI to connect technical risk concepts with supervisory expectations.
The objective is to help participants understand not only what model-risk principles require, but also why weaknesses arise and how institutions can respond.
7. Who Should Enrol in the Programme?
The programme is designed for professionals involved in the development, use, oversight, validation or assurance of models.
Primary Target Audience
- Model Risk Management Professionals and Model Validators.
- Chief Risk Officers and Enterprise Risk Management Teams.
- Credit Risk, Market Risk and Quantitative Risk Professionals.
- Risk Analytics and Model Development Teams.
- Regulatory Compliance and Risk Governance Professionals.
Relevant Professionals
- Internal Auditors and Model Assurance Professionals.
- AI/ML Governance and Data Science Teams in BFSI.
- Banking Technology and Digital Transformation Professionals.
- Treasury, ALM and Capital Planning Teams.
- NBFC, Insurance and FinTech Risk Professionals.
The programme is also relevant to professionals transitioning from credit risk, analytics, internal audit or broader enterprise risk roles into specialised Model Risk Management responsibilities.
8. What Participants Will Be Able to Do
Upon completing the programme, participants should develop the knowledge needed to:
- Identify and classify models across banking and financial functions.
- Understand RBI and international MRM frameworks, including the implications of emerging regulatory expectations.
- Evaluate model governance and responsibilities across the Three Lines Model.
- Assess model inventories, materiality and risk tiering to support proportionate oversight.
- Understand independent validation through conceptual review, back-testing, benchmarking and stress testing.
- Identify model deterioration and understand the governance of drift, overrides, recalibration and changes.
- Assess third-party and AI/ML model risks, including transparency, dependencies and performance concerns.
- Interpret model-risk dashboards and management reports to support governance and Board oversight.
These capabilities are relevant to professionals responsible for ensuring that financial models remain reliable, appropriately controlled and suitable for business use.
9. Supporting Institutional Model Risk Management Capability
The programme may also support banks and financial institutions seeking to strengthen internal MRM capability across multiple departments.
A structured learning initiative can help risk, analytics, business, compliance and audit professionals develop a shared understanding of model definitions, ownership, validation and monitoring responsibilities.
It may be particularly relevant to institutions reviewing their existing model inventories, governance frameworks, independent validation arrangements and AI-enabled decision systems in light of RBI’s evolving approach.
The purpose is to strengthen professional capability, not to suggest that completion of this course constitutes regulatory compliance or replaces an institution’s independent validation responsibilities.
10. RMAI’s Commitment to Professional Risk Management Education
The Risk Management Association of India (RMAI) is dedicated to strengthening risk management knowledge, research, professional education and institutional capability across sectors.
Through its learning platform, Smart Online Course, RMAI offers specialised programmes covering enterprise risk, operational risk, credit risk, fraud risk, technology governance, financial risk, AI risk and other emerging areas.
The new Model Risk Management in Banking & Financial Institutions programme extends this learning portfolio by addressing the governance of quantitative and technology-enabled models throughout their lifecycle.
It has been structured to complement specialised programmes in Credit Risk Management, AI Risk, IT Risk and Financial Risk Management while providing an integrated understanding of Model Risk Management as a distinct discipline.
11. Course Enquiries and Institutional Nominations
RMAI welcomes expressions of interest from individual professionals and institutions seeking specialised training in Model Risk Management.
Banks, NBFCs, financial institutions and corporate Learning & Development teams may contact RMAI to discuss programme details and institutional nominations.
Programme: Model Risk Management in Banking & Financial Institutions
Duration: 17 Hours
Curriculum: 10 Modules and 52 Lectures
Coverage: RBI and international regulations, model governance, validation, monitoring, third-party models and AI/ML model risks
For Programme Information and Nominations
Risk Management Association of India (RMAI)
Course Link
Learning Platform: https://www.smartonlinecourse.co.in/
Email: info@rmaindia.org
Conclusion
The increasing use of models in financial decision-making has made Model Risk Management an important area of professional and institutional capability.
RBI’s expanding regulatory focus, international supervisory developments and the adoption of AI-enabled financial models are reinforcing the need for comprehensive model governance, effective validation and continuous monitoring.
The launch of RMAI’s Model Risk Management in Banking & Financial Institutions programme aims to equip professionals with a structured understanding of how models should be identified, governed, challenged, monitored and managed across their lifecycle.
By integrating regulatory perspectives, practical methodologies, banking applications and real-world case studies, the programme seeks to strengthen the professional competencies required for effective Model Risk Management.
Regulatory References
- Reserve Bank of India: Draft Guidance on Regulatory Principles for Model Risk Management, 24 June 2026. RBI release summary
- Reserve Bank of India: Draft Regulatory Principles for Management of Model Risks in Credit, 5 August 2024.
- US Federal Reserve: Revised Guidance on Model Risk Management, SR 26-2, 17 April 2026
- UK Prudential Regulation Authority: SS1/23 – Model Risk Management Principles for Banks, April 2026
- OSFI Canada: Guideline E-23 – Model Risk Management
Regulatory information reviewed as of 2 November 2026. The RBI June 2026 proposal is described as draft guidance. International regulatory references are included for comparative professional education and do not necessarily apply to Indian regulated entities.