IFRS 9 Expected Credit Loss (ECL) Model Development Training Course

Description

This course equips participants with the technical knowledge and practical skills required to develop, implement, and manage Expected Credit Loss (ECL) models under IFRS 9. It focuses on credit risk modelling, staging assessment, probability of default estimation, loss given default, exposure at default, macroeconomic forecasting, and model validation. Participants will learn how to build robust ECL frameworks that comply with international financial reporting standards and support effective credit risk management.

Target Groups

Credit risk analysts and managers
Financial analysts and data scientists
Bank and microfinance institution staff
Risk management and compliance officers
IFRS reporting and accounting professionals
Internal and external auditors
Treasury and finance professionals
Regulators and supervisory staff
Consultants in credit risk and financial modeling
Students in finance, statistics, economics, or actuarial science

Course Objectives

By the end of this course, participants will be able to:

Understand IFRS 9 ECL requirements and principles
Develop and implement ECL models effectively
Estimate Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD)
Apply staging criteria (Stage 1, 2, and 3 classification)
Incorporate forward-looking macroeconomic information
Build and validate credit risk models
Perform scenario and stress testing for ECL
Interpret and report ECL results for financial statements
Strengthen model governance and documentation
Ensure compliance with IFRS 9 standards in credit provisioning

Course Modules

Module 1: Introduction to IFRS 9 and ECL Framework

Overview of IFRS 9 requirements
Concept of Expected Credit Loss
Differences between IAS 39 and IFRS 9
Credit risk and impairment principles
Overview of ECL lifecycle

Module 2: Staging Approach under IFRS 9

Stage 1, Stage 2, and Stage 3 classification
Significant increase in credit risk (SICR)
Default definition and criteria
Transition between stages
Impact of staging on impairment

Module 3: Key ECL Components (PD, LGD, EAD)

Probability of Default modelling
Loss Given Default estimation
Exposure at Default calculation
Data requirements and sources
Model calibration techniques

Module 4: Macroeconomic Forecasting and Forward-Looking Data

Role of macroeconomic variables in ECL
Scenario development (base, optimistic, pessimistic)
Weighting of economic scenarios
Linking macro factors to credit risk
Forecasting techniques and challenges

Module 5: ECL Model Development and Implementation

Model design and structure
Data preparation and segmentation
Model building techniques
Portfolio-level vs individual ECL models
Implementation in financial systems

Module 6: Model Validation and Back Testing

Model performance assessment
Back-testing methodologies
Sensitivity and stress testing
Error analysis and adjustments
Independent model validation processes

Module 7: ECL Reporting and Financial Statement Impact

Recognition of impairment losses
Presentation in financial statements
Disclosure requirements under IFRS 9
Reporting to management and regulators
Impact of ECL on profitability and capital

Module 8: Governance, Controls, and Compliance

Model governance frameworks
Internal controls over ECL models
Audit and regulatory expectations
Documentation standards
Risk management integration

Module 9: Advanced ECL Techniques and Automation

Machine learning in credit risk modelling
Big data applications in ECL
Automation of ECL calculations
Advanced statistical techniques
Digital transformation in credit risk management

Module 10: Capstone Project and Case Studies

Real-world ECL model development case studies
Group project: building a simplified IFRS 9 ECL model
Simulation of staging and provisioning decisions
Scenario analysis and stress testing exercises
Emerging trends in IFRS 9 implementation, AI-driven credit risk modelling, real-time credit monitoring systems, and advanced predictive analytics in financial risk management