IFRS 9 Expected Credit Losses Building Models for Central Banks

Introduction

The International Financial Reporting Standard 9 (IFRS 9) is a crucial accounting standard that addresses financial instruments' classification, measurement, and impairment. For central banks, understanding IFRS 9's expected credit loss (ECL) model is vital to assess and manage potential credit risks accurately. The aim to equip central bank professionals with the necessary knowledge and practical skills to build IFRS 9 ECL models from scratch using real-world data.

The primary objective of this course is to provide central bank staff with a comprehensive understanding of IFRS 9 and its ECL model. Participants will learn to build robust ECL models using practical data to support effective credit risk management in central banks' operations.

Course Objectives

• Understand IFRS 9 and its significance for central banks, along with its impact on financial reporting.
• Comprehend the Expected Credit Losses (ECL) model, including the three stages of credit impairment and its regulatory context.
• Prepare and assess data quality for ECL modeling, addressing data gaps and ensuring compliance with regulations.
• Apply ECL modeling techniques, using Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD) models.
• Calibrate and validate ECL models through stress testing, backtesting, and ensuring proper model governance.
• Integrate ECL models into risk management frameworks of central banks to enhance credit risk management.
• Analyze real-world case studies of successful ECL model implementations and their impact on financial stability

Course Outlines

Day 1: Introduction to IFRS 9 and Expected Credit Losses

• Overview of IFRS 9 and its significance for central banks.
• Key concepts of the Expected Credit Losses (ECL) model.
• Understanding the three stages of credit impairment.
• Regulatory context and global adoption of IFRS 9.

Day 2: Data Preparation and Quality Assessment

• Identifying relevant data sources for ECL modeling.
• Data cleaning, transformation, and validation techniques.
• Evaluating data quality and addressing data gaps.
• Ensuring compliance with data privacy and security regulations.

Day 3: ECL Modelling Techniques and Approaches

• Fundamental methodologies for building ECL models.
• Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD) models.
• Estimation of transition matrices and macroeconomic factors.
• Incorporating forward-looking information in ECL modeling.

Day 4: Model Calibration and Validation

• Techniques for calibrating ECL models with practical data.
• Backtesting and stress testing the ECL model.
• Validation procedures and model governance.
• Model documentation and communication with stakeholders.

Day 5: Implementing IFRS 9 ECL Models in Central Banks

• Integration of ECL models into central banks' risk management framework.
• Understanding the implications of ECL on capital adequacy and financial stability.
• Leveraging ECL insights for better decision-making in credit risk management.
• Case studies and real-world examples of successful ECL model implementations.