Regulatory Capital Modelling Training Course

Regulatory Capital Modelling Training Course

Introduction

In the current volatile and heavily regulated financial landscape, Robust Regulatory Capital Modelling is not just a compliance task but a Strategic Imperative for financial institutions. The introduction of Basel III, and the subsequent refinements, has fundamentally changed how banks and insurers must quantify, manage, and report their capital adequacy. Regulatory Capital Modelling Training Course addresses the critical need for advanced expertise in developing, validating, and implementing next-generation capital models. We dive deep into the quantitative methodologies required for accurate Risk-Weighted Asset (RWA) calculation across different risk classes, focusing on the latest regulatory updates like the Standardised Approach with Output Floor and Internal Ratings-Based (IRB) Approaches. Mastering this domain is essential for Optimizing Capital Allocation, ensuring Financial Resilience, and maintaining Regulatory Compliance in a rapidly evolving global market.

This training provides a rigorous, practical, and up-to-date framework for all professionals involved in the capital lifecycle. Participants will gain Actionable Insights into Model Governance, Stress Testing, and the crucial role of Data Quality and Advanced Analytics including Machine Learning applications in enhancing model performance and predictive power. By synthesizing Quantitative Finance with regulatory requirements, the course equips practitioners with the tools to navigate complex regulatory environments, drive Capital Efficiency, and communicate model results effectively to senior management and supervisors. Ultimately, the goal is to transform regulatory obligations from a burden into a competitive advantage through Best-Practice Capital Management.

Course Duration

5 days

Course Objectives

1. Master the latest global regulatory standards for Capital Adequacy and minimum capital requirements.
2. Design and validate Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default models under the Internal Ratings-Based Approach.
3. Apply Statistical Significance Testing and Model Performance Metrics to ensure model accuracy and compliance with supervisory expectations.
4. Calculate and model Operational Risk Weighted Assets using the new Standardised Approach (SA) and assess the impact of Model Risk Management.
5. Understand the calculation of Market Risk Capital under the Fundamental Review of the Trading Book, including the Standardised Approach and Internal Model Approach
6. Develop a robust Internal Capital Adequacy Assessment Process and Internal Liquidity Adequacy Assessment Process incorporating key Pillar 2 risks.
7. Conduct effective Macroeconomic Stress Testing and Reverse Stress Testing to assess capital resilience under extreme but plausible scenarios.
8. Establish a strong Data Governance Framework for model inputs, focusing on Data Lineage, quality assessment, and compliance with BCBS 239.
9. Measure and mitigate Model Risk associated with regulatory models, covering model Design, Implementation, and Use limitations.
10. Identify and execute strategies for Capital Efficiency and balance sheet management based on accurate RWA figures and regulatory interpretation.
11. Prepare comprehensive and transparent Pillar 3 Disclosures to meet market discipline requirements and enhance stakeholder trust.
12. Explore the application of Machine Learning (ML) and Advanced Econometrics for improving predictive power in core risk parameters
13. Incorporate Climate-Related Financial Risk and broader ESG Factors into capital models and stress testing exercises

Target Audience

1. Risk Management Professionals
2. Regulatory Reporting Specialists
3. Capital/Treasury Management Teams
4. Model Developers & Validators
5. Internal & External Auditors
6. Quantitative Analysts (Quants)
7. Financial Regulators and Supervisors
8. Consultants specializing in Financial Risk

Course Modules

Module 1: Foundational Regulatory Framework (Basel III & IV)

• Key Regulatory Capital Concepts
• Overview of Basel Accords.
• Three Pillars of Basel
• The Output Floor Mechanism.
• Model Governance and Lifecycle.
• Case Study: Analyzing the capital impact of the Standardised Output Floor on a European bank's mortgage portfolio, comparing IRB-calculated RWA vs. the floor.

Module 2: Credit Risk RWA: Standardised Approach (SA)

• SA Methodologies.
• External Credit Assessment Institutions (ECAIs).
• Credit Risk Mitigation (CRM).
• SA-CCR for Counterparty Risk
• Securitisation Framework
• Case Study: Calculating the capital charge for a trade finance portfolio using the simplified and complex Standardised Approach with various Credit Risk Mitigants.

Module 3: Credit Risk RWA: Internal Ratings-Based (IRB) Approaches

• Foundation and Advanced IRB.
• PD Modelling.
• LGD Modelling.
• EAD Modelling.
• Through-the-Cycle (TTC) and Point-in-Time (PIT).
• Case Study: Building a basic Logistic Regression PD Model for a retail lending portfolio, including calibration and backtesting.

Module 4: Market Risk Capital & FRTB

• Trading Book and Banking Book.
• FRTB Standardised Approach (SA).
• FRTB Internal Model Approach (IMA).
• Backtesting & P&L Attribution.
• Capital for Specific Instruments.
• Case Study: Calculating the Market Risk RWA for a fixed-income trading desk using both the SA-SBM and the IMA-Expected Shortfall approaches.

Module 5: Operational Risk Capital & Standardised Approach (SA)

• Evolution of OpRisk Capital
• Business Indicator (BI) & Component Calculation.
• Loss Component (LC) & ILM
• Operational Risk Management & KRIs
• Scenario Analysis in OpRisk.
• Case Study: Calculating the Operational Risk RWA for a large financial institution using the Basel SA formula, including all three sub-components of the Business Indicator.

Module 6: Pillar 2, ICAAP, and Liquidity Risk

• Pillar 2 Supervisory Review Process (SREP).
• ICAAP Design and Methodology.
• Non-Pillar 1 Risks.
• Stress Testing Governance.
• Liquidity Risk Management & ILAAP
• Case Study: Designing a Reverse Stress Test scenario for a bank that leads to the breach of its minimum CET1 ratio, identifying the key risk drivers.

Module 7: Model Risk Management (MRM)

• BCBS 239 Principles.
• Model Documentation Standards
• Model Validation Lifecycle
• Challenging the Model.
• Quantifying Model Uncertainty.
• Case Study: Reviewing a Model Validation Report for an LGD model, identifying critical findings, and proposing effective remediation actions.

Module 8: Capital Modelling Technologies and Future Trends

• Data Architecture for Modelling.
• Advanced Analytics & Model Performance.
• Cloud Computing in Capital Calculation.
• Interoperability and Automation.
• ESG & Climate Risk Modelling.
• Case Study: Exploring a hypothetical application of an AI-driven PD model for a new asset class, focusing on Explainability (XAI) and Model Bias concerns for regulatory submission.

Training Methodology

This course employs a participatory and hands-on approach to ensure practical learning, including:

• Interactive lectures and presentations.
• Group discussions and brainstorming sessions.
• Hands-on exercises using real-world datasets.
• Role-playing and scenario-based simulations.
• Analysis of case studies to bridge theory and practice.
• Peer-to-peer learning and networking.
• Expert-led Q&A sessions.
• Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.