IFRS 9, ECL, Credit Risk & Risk Modelling Articles
Stay informed with practical articles covering IFRS 9, expected credit loss, risk modelling, regulatory compliance, and industry best practices.
Why Should Financial Institutions Avoid Black-Box IFRS 9 Solutions?
Is Your IFRS 9 Process a Black Box? Uncover the hidden complexities for clearer, more effective credit risk decisions.
Learn MoreJacob–Frye PIT LGD: From Concept to Practical Application
This article explains how the Jacob–Frye approach can be applied in practice to derive Point-in-Time LGD under IFRS 9, linking recovery behavior to changing macroeconomic conditions. Using a consumer portfolio example, we show how PIT LGD responds to economic stress while remaining stable, explainable, and anchored to long-run recovery assumptions—supporting robust and audit-ready ECL outcomes.
Learn MoreIFRS 9 Impairment: A Practical Guide to Building an ECL Model
A step-by-step guide to building an IFRS 9 impairment model, from portfolio segmentation and PD, LGD and EAD to macroeconomic scenarios, validation and documentation.
Learn MoreHow to Calculate Lifetime Expected Credit Loss Under IFRS 9
Learn how lifetime ECL is calculated under IFRS 9, including expected life, PD term structures, future EAD, LGD, forward-looking information and discounting.
Learn More12-Month ECL vs Lifetime ECL: Key Differences Explained
Understand the difference between 12-month and lifetime ECL, how each links to IFRS 9 stages, and what changes in the ECL calculation.
Learn MoreIFRS 9 Staging Explained: Stage 1, Stage 2 and Stage 3 ECL
A practical explanation of IFRS 9 Stage 1, Stage 2 and Stage 3, how SICR drives stage movements, and why staging criteria matter for impairment.
Learn MoreSignificant Increase in Credit Risk (SICR): Methods and Examples
Explore common SICR assessment methods under IFRS 9, including PD changes, rating migration, days past due and qualitative indicators, with a practical example.
Learn MorePD, LGD and EAD Explained: How the Three Inputs Drive ECL
Learn what Probability of Default, Loss Given Default and Exposure at Default measure, and how they work together to produce IFRS 9 Expected Credit Loss.
Learn MoreHow to Estimate Probability of Default for Credit Risk Models
An overview of PD modelling methods, including observed default rates, roll-rate analysis, migration matrices and Pluto-Tasche, and how historical PD becomes IFRS 9 PD.
Learn MoreLGD Modelling: Methods, Data Requirements and Validation
Discover how Loss Given Default is modelled under IFRS 9, from workout and collateral-based LGD to data requirements and model validation.
Learn MoreEAD Modelling Explained: Exposure at Default Under IFRS 9
Learn how Exposure at Default is modelled for amortizing, revolving and bullet products under IFRS 9, including CCFs, data needs and EAD term structures.
Learn MoreHow Macroeconomic Scenarios Affect IFRS 9 ECL
See how macroeconomic variables and multiple economic scenarios make IFRS 9 ECL forward-looking, and how scenario weights shape the final estimate.
Learn MoreProbability-Weighted ECL: Methodology, Scenarios and Calculation
Understand how probability-weighted ECL combines baseline, upside and downside scenarios into one estimate, with a worked calculation and guidance on weights.
Learn MoreCredit Risk Model Validation: What Banks Should Test
A guide to what banks should test when validating credit risk models, from data quality and performance to calibration, stability and forward-looking adjustments.
Learn MoreCredit Risk Model Calibration: A Practical Guide
Learn when PD calibration is needed, practical techniques such as Logit, Quasi-Moment Matching and Binomial calibration, and how to test calibrated PDs.
Learn MoreBacktesting Credit Risk Models: Methods, Metrics and Best Practices
Learn how to backtest PD, LGD, EAD and ECL estimates against actual outcomes, which metrics to use, and how backtesting fits into model validation.
Learn MoreCredit Risk Model Monitoring: What to Monitor After Validation
Discover what banks should monitor after model validation, including performance, population stability, data quality, overrides and forward-looking behavior.
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