Expected Credit Loss Modelling under IFRS 9

Introduction

The expected credit loss model requires banks, finance companies and many corporates to recognise credit losses earlier, based on forward-looking information rather than waiting for losses to occur. Implementing it well demands a combination of accounting knowledge, credit risk understanding, data management and statistical modelling.

This training course explains the expected credit loss requirements of IFRS 9 and how they are applied in practice. Participants learn the staging approach, the definition of default, significant increase in credit risk, and how probability of default, loss given default and exposure at default are estimated and combined with macroeconomic scenarios.

The course also covers simplified approaches for trade receivables, governance and validation of models, management overlays, disclosures and the audit and regulatory scrutiny that expected credit loss estimates attract.

Course Objectives

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

• Explain the classification and impairment principles of IFRS 9
• Apply the three-stage model and criteria for significant increase in credit risk
• Define default and credit-impaired status consistently with risk management
• Understand the estimation of probability of default, loss given default and exposure at default
• Incorporate forward-looking macroeconomic scenarios into expected credit loss
• Apply the simplified approach for trade receivables and contract assets
• Establish governance, validation and management overlay processes
• Prepare clear expected credit loss disclosures and support audit reviews

Target Audience

This training course is suitable for a wide range of professionals, including:

• Financial reporting and accounting professionals in banks and finance companies
• Credit risk modellers and analysts
• Risk managers and chief risk officers' teams
• Corporate accountants managing receivables impairment
• Internal and external auditors
• Regulators and supervisors
• Finance and risk transformation consultants

Training Methodology

The course combines technical explanations with spreadsheet-based modelling exercises, worked staging examples and case studies of disclosure practice. Participants build a simplified expected credit loss model, test scenario sensitivity and discuss governance challenges in small groups.

Target Competencies

• IFRS 9 impairment knowledge
• Credit risk parameter estimation
• Scenario-based loss modelling
• Model governance and validation
• Financial disclosure preparation
• Finance and risk collaboration

Day One: IFRS 9 Framework and Impairment Principles

• Why the Impairment Model Changed: Lessons from incurred loss accounting
• Classification and Measurement Overview: Business models and cash flow characteristics
• Scope of Impairment: Loans, debt securities, receivables and commitments
• The General Approach: Twelve-month and lifetime expected losses
• Key Concepts: Default, credit-impaired assets and write-offs
• Finance and Risk Roles: Data, models and accounting judgement
• Case Study: Reviewing an institution's impairment policy

Day Two: Staging and Significant Increase in Credit Risk

• Stage Allocation: Criteria and practical approaches
• Quantitative Indicators: Lifetime PD changes and thresholds
• Qualitative Indicators: Watch lists, forbearance and sector risks
• Backstop Indicators: Days past due presumptions
• Transfers Between Stages: Cure periods and movement analysis
• Low Credit Risk Exemption: Appropriate use
• Practical Exercise: Allocating a loan portfolio to stages

Day Three: Risk Parameters and Modelling

• Probability of Default: Point-in-time and lifetime estimation
• Loss Given Default: Collateral, recoveries and cure rates
• Exposure at Default: Amortisation, prepayment and credit conversion factors
• Discounting and Effective Interest Rates
• Data Requirements: History, quality and proxies
• Simplified and Provision Matrix Approaches for Receivables
• Workshop: Building an expected credit loss calculation

Day Four: Forward-Looking Information

• Macroeconomic Scenarios: Selecting variables and weights
• Linking Macro Factors to Credit Parameters: Satellite models
• Scenario Probability Weighting: Non-linearity considerations
• Management Overlays: When they are justified and how to document them
• Sensitivity Analysis: Understanding drivers of provision changes
• Stress Events and Uncertainty: Lessons from recent economic shocks
• Practical Exercise: Applying scenarios and calculating a weighted provision

Day Five: Governance, Disclosure and Audit

• Model Governance: Ownership, approval and change control
• Model Validation and Back-Testing: Assessing performance
• Disclosures: Reconciliations, credit risk exposures and judgements
• Audit Expectations: Evidence, controls and challenge
• Regulatory Interaction: Supervisory views on provisioning
• Continuous Improvement: Data, automation and reporting efficiency
• Final Exercise: Presenting provision results to an audit committee