IFRS 9 Starter Pack: Credit Risk & ECL Basics (English)

Explore IFRS 9 staging rules, distinguishing Stage 1, Stage 2, and Stage 3 and when to apply twelve-month versus lifetime expected credit losses.

Understand how SICR moves exposures from stage 1 to stage 2 under IFRS 9, triggering lifetime ECL and using quantitative, qualitative, and 30 days past due indicators.

Define default triggers under IFRS 9, including ninety days past due and unlikeliness to pay, with qualitative events like bankruptcy or restructuring, to determine Stage 3 and Basel-aligned lifetime ECL.

Apply IFRS 9 staging to a three-loan portfolio, moving borrowers A, B, and C through stages 1–3 with 12-month and lifetime expected credit losses, illustrating dynamic provisioning and default progression.

Explore how macroeconomic scenarios drive forward-looking expected credit losses under IFRS 9, using base, upside, and downside cases weighted to reveal credit risk under uncertainty.

Explore management overlays in IFRS 9 credit loss modeling, applying post-model adjustments to address data gaps, emerging risks, and scenario limitations. Ensure governance and approvals to maintain transparent provisions.

Compare IFRS 9 and Basel IRB, showing how IFRS 9 uses staging and forward-looking, probability-weighted lifetime ECL for accounting transparency, while Basel IRB governs regulatory capital with a one-year horizon.

Governance pillars for IFRS 9 models—development standards, validation, monitoring, and committees—keep provisions credible by ensuring transparent, reliable risk estimates and addressing data risk.

Build a simple IFRS 9 ECL model in Excel by combining EAD, PD, and LGD to calculate the ECL for sample loans and a portfolio with scenario weights.

Explore IFRS 9 expected credit loss, including PD, LGD, and EAD, the three-stage model, macroeconomic scenarios, governance, and an Excel demo for next steps.

Examine an IFRS 9 case study from COVID-19, detailing staging decisions, macroeconomic scenarios, and management overlays that shaped credit risk provisioning.

Learn how to prepare for IFRS 9 interviews and apply the expected credit loss framework in real-world risk management, covering IAS 39 differences, staging, and the PD LGD EAD formula.

Explore the three pillars of IFRS 9—classification, measurement, and impairment. Learn how the business model and SPPI tests determine asset classification and how the forward-looking ECL framework drives impairment.

Explore IFRS 9 classification pillars and the business model and SPI tests, and how financial assets and liabilities are measured as amortized cost, FVOCI, or FVTPL.

Compute 12-month and lifetime ECLs using forward-looking PD, LGD, and EAD across stages, aggregate at portfolio level, and produce regulatory reports with discounting and disclosures of assumptions and sensitivity analyses.

Relate IFRS 9 expected credit loss to prudential capital concepts, and outline governance, data lineage, and reconciliations for regulatory ECL reporting and pillar three disclosures.

Explore the twelve month expected credit loss under IFRS nine, identify stage one assets, and apply the PD times LGD times EAD formula with a practical example.

Explore how IFRS 9 impairment uses forward-looking expected credit loss, driven by PD, LGD, and EAD across three stages, and its profit-and-loss and regulatory implications.

Explore lifetime expected credit loss (ECL) concepts under IFRS 9, applying the multi-period formula with PD, LGD, EAD, and discounting to various financial instruments.

Explore advanced portfolio aggregation for IFRS 9 ECL, using weighted averages of PD, LGD, and EAD across stages, with trend, scenario, and concentration insights for regulatory reporting and management decisions.

Define lifetime ECL and distinguish it from 12 month ECL; apply the multi-period formula with PDS, LGD, EAD, DFT, and discounting across instruments, including forward looking scenarios.

Master IFRS 9 regulatory reporting and disclosures, including ECL, staging, macro scenarios, and assumptions, to transparently communicate outcomes to regulators, auditors, and investors.

Explore sensitivity analysis in IFRS 9 ECL modeling to improve transparent disclosures, and quantify how assumption changes affect ECL for IFRS 7 and IFRS 9 compliant notes.

Enhance IFRS 9 credit risk management by implementing continuous model validation and monitoring, covering discrimination and calibration metrics, backtesting, benchmarking, ECL, and a robust trigger framework.

Assess credit risk models with a robust, multifaceted framework that goes beyond accuracy, using confusion matrix metrics, AUC, KS, Gini, calibration (Hosmer-Lemeshow), Gaines, lift, PSI, and stability over time.

Explore how the ROC curve, AUC, and C statistic measure discrimination between defaulters and non-defaulters in IFRS 9 credit risk models, with interpretation, calculation, and best practice.

Assess discrimination, calibration, and stability metrics for IFRS 9 credit risk models. Identify AUC, Gini, KS, Brier score, PSI, and model selection criteria for development, validation, and monitoring.

Describe observation and performance windows in IFRS 9 credit risk modeling, showing how non-overlapping lookback predicts defaults while avoiding data leakage and aligning with Basel guidelines.

Track loan performance over time by grouping accounts by origination vintage to reveal trends in default, delinquency, and loss rates for risk management and IFRS 9 ECL.

Explore wholesale variables for IFRS 9 and Basel IRB–aligned PD modeling, detailing borrower characteristics, financial ratios, facility features, behavioral patterns, and macroeconomic indicators to enable forward-looking risk estimates.

Explore retail credit risk model variables that drive PD, LGD, and EAD for mortgages, credit cards, and personal loans.

Ensure IFRS 9 data quality across PD, LGD, and EDI by applying six dimensions—accuracy, completeness, consistency, timeliness, validity, and uniqueness—supported by automated checks, lineage, and governance to improve regulatory compliance.

Build IFRS 9 credit risk models in Python for point-in-time and lifetime PDs and ECL using a 20-row synthetic dataset, with feature engineering and pandas workflows.

Build an IFRS 9 credit risk model in Excel using borrower data and macroeconomic drivers, applying logistic regression to estimate 12-month PD and lifetime ECL with PD, LGD, and EAD.

Explore IFRS 9 foundations, including the PIT-PD concept, the three-stage impairment model, and the ECL formula PD × LGD × EAD, with practical Excel workflows.

Explore how IFRS 9 credit risk modeling in Excel moves from raw data to PD, staging, and ECL, using the workbook's raw data, control, and calculation sheets.

Verify data quality flags in the workbook and flag missing age, age range 18–100, negative dpd, out-of-range utilization 0–5, and duplicates to ensure clean data for pd modeling and ecl.

Explore the raw data sheet as the source of truth for IFRS 9 modeling, capturing borrower demographics, behavior metrics, and macroeconomic context to estimate PD, staging, and ECL.

Examine how GDP growth, unemployment, and interest rates shape IFRS 9's forward-looking credit risk. Scale macro variables with z scores, and use scenario weighting and lags to inform PD.

Understand the control sheet as the dashboard that stores coefficients, intercept, scaling parameters, and macro scenarios to compute the PD via a logistic function, with calibration documented.

Scale continuous variables to z-scores, create unemployment and unsecured flags, apply winsorization and imputation, and derive credit utilization and DPD buckets for auditable PD inputs.

Explore the IFRS 9 three-stage impairment model, linking probability of default to expected credit losses, with 12-month and lifetime ECLs and SICR triggers.

Compute forward-looking ECL in Excel by modeling PD, LGD, and EAD across time with scenario weighting under IFRS 9, including discounting and stage transitions.

Explore the end-to-end ECL process from data intake and quality checks to PD estimation, staging, and discounted ECL reporting under IFRS 9, with transparent, auditable Excel workflows.

Explore the three pillars of modern credit risk: probability of default, loss given default, and exposure at default, and how they drive IFRS 9 expected credit losses and Basel capital.

Build a model-ready data set for PD, LGD and EAD by implementing a single wide table, macro-aligned inputs, and rigorous data quality checks with full documentation for IFRS 9 compliance.

Develop and calibrate PD, LGD, and EAD models for IFRS 9 and Basel, using segmentation, model selection, estimation, calibration, and validation. Incorporate macroeconomic scenarios and downturn adjustments for forward-looking estimates.

Explore scenario based forecasting in IFRS 9 staging, linking macroeconomic drivers to PD, LGD, and EAD to produce probability weighted ECL across baseline, downside, and severe scenarios.

Compute ECL under IFRS 9 using PD, LGD and EAD across stages 1–3 with scenario-weighted, discounted estimates, then aggregate for portfolio reporting, disclosures and governance dashboards.

Explore how ECL sensitivity, scenario stress testing, and reverse stress testing integrate IFRS nine with ICA, translating provisions into robust capital planning under adverse conditions.

Explore IFRS 9 model monitoring and backtesting to ensure accuracy, stability, and compliance. Learn data quality checks, PSI-driven drift detection, PD/LGD/EAD performance measures, backtesting steps, and governance-driven actions.

Recalibrate and redevelop IFRS 9 credit risk models to maintain accuracy amid data shifts, governance, and documentation, with triggers and full redevelopment when needed.

Integrate IFRS 9 model life cycle with Basel 3.1 integration to connect expected credit losses and capital adequacy through governance, stress testing, and continuous improvement.

Explore the end-to-end IFRS 9 modeling framework from data sourcing to capital integration, linking PD, LGD, and EAD with scenario-based forecasting and ECL, aligned with Basel 3.1 and ICP.

Explore internal and external data sources for IFRS 9 models, learn how PD, LGD, and EAD drive ECL estimates, and master data architecture and governance.

Drive IFRS 9 data quality by ensuring accuracy, completeness, consistency, and timeliness in PD, LGD, and EAD inputs for reliable ECL modeling, supported by testing, control reports, and documentation.

Develop and sustain a robust IFRS nine model governance framework for credit risk and ECL estimation. Define roles, lifecycle stages, and documentation to ensure transparency, accountability, and regulatory compliance.

Explore how controls and monitoring in IFRS 9 model governance ensure data quality, accuracy, transparency, and compliance across the model life cycle through input, process, and output controls.

Explore how data governance, data quality, and layered controls support accurate IFRS 9 ECL modeling and auditable, regulator-ready credit risk estimates.

Learn to transform raw data into a data quality dashboard in power BI, flag missing values and anomalies, and publish dashboards for governance and organization-wide sharing.

Learn how IFRS nine calculates expected credit losses and how IFRS seven discloses them with stage by stage reporting, reconciliations, and qualitative notes.

Trace the movement of expected credit losses from opening to closing balance under IFRS 9. See how charge, release, and write-offs transform the allowance across stages, enabling transparent impairment reporting.

Explore sensitivity analysis and scenario reporting under IFRS 9 to understand probability-weighted ECL across base, upside, and downside scenarios. Test how PD, LGD, and EAD inputs influence the final ACL.

Explore management commentary and the auditor's view on IFRS nine reporting, linking qualitative disclosures to ECL results and assessing assumptions, governance, and sensitivity.

Explore an IFRS 9 ECL reporting Excel template that structures stage-based allowances, links to financial statements, and supports audit-ready disclosure through standardized, reconciled templates.

Explore IFRS nine software and tools from Excel prototyping to SAS, Python, and Power BI, and learn how to select, automate, and govern data and reporting for scalable credit risk.

Choose the right IFRS 9 tools using a four-step framework, balancing capability, scalability, governance, and integration across Excel, SAS, Python, and R.

Boost IFRS 9 workflows with version control, documentation, automation, AI copilots, and visualization to create an auditable, transparent, future-ready environment for credit risk and ECL modeling.

This IFRS 9 implementation roadmap takes you from Excel basics to enterprise automation with SAS Viya, Python, and R, emphasizing governance, data analytics, and scalable PD, LGD, and EAD modeling.

Explore the IFRS 9 tools and implementation pathway from Excel to SAS, Python, Power BI, and APIs, integrating PD, LGD, and EAD with governance and automation.

Explore how IFRS nine credit risk models are built in Excel, focusing on data quality, dashboards, and rule-based checks to ensure transparent audit trails and accurate ECL projections.

Apply calibration and adjustments in IFRS 9 models using scaling factors or Platt scaling in Excel, and rerun the model when discrimination or stability falters.

Assess model validation and performance measurement for IFRS 9 credit risk by comparing predicted and observed defaults and applying Brier score, Gini, PSI to gauge calibration, stability, and ECL calculations.

Prepare data for IFRS 9 default modeling by checking missing values and invalid entries, creating derived features in Excel, and splitting data into development and validation sets.

Explore how to evaluate IFRS 9 probability-of-default models with accuracy, calibration, discrimination, and stability in Excel, using deciles, cutoff rules, Gini/ROC, and population stability index.

Build IFRS 9 credit risk models in Excel from raw borrower data with macroeconomic drivers to forecast 12-month PD and lifetime ECL using logistic regression, PD, LGD, and EAD.

Explore IFRS 9 and the forward-looking expected credit loss framework, using Excel to model the three-stage impairment model, PIT PD, EAD, LGD, and ECL calculations.

Verify data quality flags in the workbook, including missing age, age range 18–100, negative DPD, credit utilization 0–5, and duplicates to ensure clean data for PD modeling and ECL.

Discover how the raw data sheet powers IFRS 9 credit risk modeling by capturing borrower demographics, behavior, and macroeconomic inputs to estimate default probabilities and expected credit losses.

See how macroeconomic drivers like GDP growth, unemployment, and interest rates influence IFRS 9 forward-looking PDs, with z-score scaling, lags, caps, and base, upside, downside scenario probabilities.

Explore the control sheet as the dashboard for IFRS 9 credit risk, storing coefficients, intercept, scaling parameters, and macro scenarios to compute pd via a linear score and logistic function.

Scale continuous variables to z-scores using control sheet means and stds, apply Windsorize to outliers, create unemployment and unsecured flags, impute missing values, and derive credit utilization and DPD indicators.

Explain IFRS 9 staging and how default probability drives ECL across a three-stage impairment model, from 12‑month ECL in stage one to lifetime ECL in stages two and three.

Compute forward-looking ECL under IFRS 9 by integrating PD, LGD, and EAD across time, scenarios, and discounting.

Demonstrates an end-to-end ecl workflow from raw data to final acl and reporting, with data quality checks, pd estimation, staging, and scenario-based discounting.

Examine how monetary policy transitions, technological disruption, and geopolitical fragmentation reshape current financial markets, highlighting inflation, funding stress, systemic risk, IFRS 9 and ECL under Basel 3.1 and Basel four.

Explore how central banks' monetary policy shapes liquidity, inflation, asset valuations, and interest rate risk, and how the transmission mechanism drives risk management essentials.

Climate risk becomes a systemic financial issue as banks and regulators embed physical and transition risk, using scenario analysis to guide capital, pricing, and green finance strategies.

Explore geopolitical and sovereign risk shaping modern finance, including policy choices and interdependence, and apply scenario analysis to manage market volatility, supply chain shocks, and sanctions.

Explore how fintech and digital transformation reshape financial services with ai, data analytics and cloud computing, while addressing cyber, regulatory, and operational risk.

Explore crypto assets and DeFi, including tokens, smart contracts, and decentralized exchanges, and examine valuation, custody, regulation, cyber risks, and systemic exposures for risk professionals.

Explore how systemic risk spreads through the financial system via contagion and liquidity shocks, and examine macroprudential tools that build resilience.

Explore Basel 3.1 reforms, including the output floor and revised risk weighted assets models, to strengthen risk sensitivity, model governance, and capital adequacy amid climate and esg considerations.

Explore market and funding liquidity, their crisis dynamics, and central bank interventions like quantitative easing and repo facilities that stabilize markets and prevent liquidity spirals.

Explore how technology, data, governance, and culture reshape modern risk management for proactive, enterprise-wide resilience.

Load and clean model data in SAS to compute end-to-end acl using point-in-time pd, lgd, and ead, with csv import, macro diagnostics, and observation and performance windows.

Apply data quality corrections and define a 12-month forward default flag in SAS, then assess data completeness with proc freq and proc means, delivering a clean PD modeling dataset.

Split SAS data into training, validation, and test sets with a time-based cutoff using the report date, ensuring forward-looking, leakage-free IFRS 9 Basel 3.1 model evaluation.

Apply feature engineering to transform credit predictors into informative variables that capture non-linear patterns and delinquency signals, preparing train, valid, and test data for logistic regression.

Build a 12 month point-in-time default model with logistic regression, using proc logistic on training data to assess convergence, significance, and drivers like score, days past due, and unemployment.

Assess the predictive power of a 12-month default probability with ROC AUC, K-S statistics, and logistic regression to measure discrimination on unseen data.

Perform a calibration check by comparing predicted pd to observed defaults across deciles, using a calibration curve to confirm stability and interpretability before applying the overlay and downstream api steps.

Calculate 12-month expected credit losses using PD, LGD, and EAD, normalize LGD, and perform data quality checks to produce a portfolio-level ECL suitable for governance.