IFRS 9 PIT PD: Credit Risk Modeling in Excel

Learn data preparation and quality checks for IFRS 9 PIT PD modeling, including data sources, cleaning, transformation, and backtesting to ensure accurate, compliant PD estimates.

Prepare data for IFRS 9 pd modeling by gathering loan book and macroeconomic data, cleaning and validating records, and defining default flags and observation windows.

Explore how the observation window and performance window govern predictor and target data in IFRS 9 PD models, preventing data leakage and ensuring compliant, forward-looking credit risk assessment.

Explore how observation and performance windows define IFRS 9 pit pd models, ensuring temporal separation, preventing data leakage, and aligning with regulatory and business needs.

Track loan performance over time by origination vintages to reveal trends in default, delinquency, and loss rates, supporting IFRS 9 expected credit loss modeling and Basel backtesting.

Explore wholesale variables for IFRS 9 PIT PD modeling in Excel, including borrower characteristics, financial ratios, facility details, macroeconomic indicators, and qualitative judgments for forward-looking risk assessment.

Explore retail credit risk model variables driving PD, LGD, and EAD, including demographic, application, behavioral, and macroeconomic factors, with emphasis on variable selection, transformation, and regulatory considerations under IFRS 9.

Boost default recall in credit risk modeling by applying oversampling techniques such as random oversampling and smote on training data, while avoiding overfitting and data leakage.

Explore data quality essentials for IFRS 9, focusing on accuracy, completeness, consistency, validity, uniqueness, and timeliness to ensure accurate expected credit loss calculations, stage classifications, and regulatory compliance.

Apply data quality checks in Excel to validate completeness, validity, consistency, outliers, and uniqueness before IFRS 9 probability of default modeling and ACL outcomes.

Transform raw probability of default data into a Power BI dashboard for IFRS 9 credit risk, cleaning data, creating age bands, and delivering interactive, governance-focused insights.

Identify MCAR, MAR, and NMAR as missing data mechanisms in credit risk modeling, apply flagging and imputation, and document IFRS 9 compliant strategies for accurate PD estimates and ECL.

Explore missing data treatments for financial modeling under IFRS 9, including missing value indicators, hot deck, regression, and cluster-based imputations, with emphasis on transparency and documentation.

Develop data quality and preparation for IFRS 9 PIT PD credit risk modeling, covering performance and observation windows, vintage analysis, wholesale and retail variables, oversampling, and imputations for model-ready data.

Demonstrates building a data quality dashboard in Power BI from raw Excel data to monitor missing values, anomalies, and data integrity for IFRS 9 credit risk modeling.

Explore variable selection criteria and logistic regression model development for IFRS 9 PD, using borrower, loan, macroeconomic, and behavioral data with regulatory alignment.

Explore segmentation and exploratory data analysis to understand portfolios, then transform variables using weight of evidence, binning, and stability checks for robust credit risk modeling in Excel.

Learn to detect multicollinearity in credit risk models using variance inflation factor (VIF) and variance information number (Vin), and apply remedies like removal or regularization to improve interpretability and stability.

Assess variable relevance in IFRS 9 pit PD models by measuring predictive power with IV and WOE, validating with model‑based and stability analyses across economic cycles.

Identify relevant variables for inclusion in a statistical model through variable selection, balancing evidence with business relevance to improve performance, interpretability, and IFRS 9 compliance, using expert judgment.

Explore how interaction terms capture non-additive effects between predictors to reflect macroeconomic amplification of borrower risk in pi-pd models under IFRS 9, including continuous, categorical, and region and industry interactions.

Explore logistic regression for credit risk modeling under IFRS 9 and Basel frameworks, predicting default probability with interpretable log-odds and regulator-friendly evaluation.

Explore logistic regression for point-in-time probability of default modeling, incorporating borrower variables, macroeconomic factors, interactions, and backward selection, with fit, validation, scoring, and monitoring for IFRS 9.

Master fine classing and coarse classing to convert raw variables into stable, interpretable data bins for predictive modeling, with validation using information value and population stability index.

Master handling of categorical variables in credit risk modeling by merging rare categories, applying regularization, and encoding schemes to preserve predictive power and stability.

Develop a defensible, explainable credit risk model in Excel by applying eda, missingness, and feature engineering from deciles to coarse bands, with empirical logit and a train-test split.

Evaluate IFRS 9 PIT PD credit risk models with key metrics: k-s, gini, AUC, and brier score, plus backtesting and stability checks, ensuring compliant validation documentation.

Assess model performance with a robust framework of advanced metrics beyond accuracy, including confusion matrix indicators, AUC, Gini, KS, and PSI, to ensure stability across data sets and time.

Explore the Hosmer-Lemeshow test for calibrating IFRS 9 probability of default models, using deciles, chi-square evaluation, and p-value interpretation to validate PD predictions and stage allocation.

Use the Brier score to measure probabilistic prediction accuracy by comparing predicted PDs to actual defaults. It clarifies calibration and refinement for IFRS 9 credit risk modeling and metrics.

Learn how the Kolmogorov–Smirnov k-s statistic measures credit risk model discrimination, how to calculate it from predicted pds, and its IFRS 9 validation role alongside auc and gini.

Analyze the ROC curve and C statistic (AUC) to assess IFRS 9 credit risk models' discrimination between defaulters and non-defaulters, considering calibration, thresholds, and regulatory presentation.

Analyze the Gini coefficient, ROC/AUC, Gaines chart, and lift curve in Excel to assess discrimination power for IFRS 9 credit risk models and PD validation.

Rank accounts by predicted default to form ten deciles and evaluate model performance with cumulative default distribution, gains, and lift curves for IFRS 9 credit risk validation.

Explore AIC, BIC, and negative two log likelihood as model selection criteria in credit risk modeling, balancing fit, complexity, and interpretability for IFRS 9 PD estimates.

Explore the D statistic (somer's d) as rank correlation between predicted defaults and actual outcomes, and relate it to concordance, discordance, AUC, and the Gini in IFRS 9 credit risk.

Explore how the population stability index (psi) in Excel measures model stability, detects data drift, and flags portfolio shifts in IFRS 9 credit risk models.

Explains validation techniques for logistic regression in IFRS 9 credit risk, detailing rerun and scoring methods, assessing coefficient stability, predictive performance, and calibration for regulatory compliance.

Assess IFRS 9 credit risk modeling performance through discrimination, calibration, and stability metrics. Learn how AUC, Gini, k-s, Hosmer-Lemeshow, Brier, PSI, and information criteria guide development, validation, and monitoring.

Develop robust IFRS 9 credit risk models by applying cross validation to prevent overfitting, ensure stable out-of-sample performance, and provide governance evidence across economic cycles.

Learn how to evaluate credit risk models beyond development by using discrimination, calibration, and business impact metrics aligned with IFRS 9, ensuring accurate ECL and governance readiness.

Explore validation and performance measurement for IFRS 9 PD models in Excel, comparing predicted and observed defaults, assessing calibration and stability with Brier score, Gini, and PSI.

Prepare data for IFRS 9 pit PD modeling in Excel by checking missing values and formats, creating derived features, and splitting the data 70/30 with rand for development and validation.

Evaluate the performance of a default probability model in Excel by examining accuracy, calibration, discrimination, and stability for a well-calibrated, discriminatory, and stable credit risk model.

Apply the Hosmer-Lemeshow test to assess calibration by comparing predicted default probabilities with observed defaults across deciles in Excel, using chi-square statistics and p-values to confirm good calibration.

Explore calibration and discrimination in IFRS 9 credit risk modeling using Excel, with ROC curves, AUC 0.78, Gaines and lift charts, and decile ranking.

Explore calibration and discrimination for IFRS 9 credit risk models using ROC curves, AUC, the Gaines chart, lift charts, and decile ranking in Excel.

Evaluate model fit and separation for IFRS 9 PIT PD in Excel by comparing log likelihood, AIC, and BIC, and compute the D statistic to rank defaulters vs non-defaulters.

Explore the population stability index (psi) to monitor credit risk model stability over time, compare base 2018–2019 vs current 2022 decile distributions, and interpret psi trends.

Consolidate calibration, discrimination, and stability metrics into an Excel performance summary with benchmarks and interpretations for senior management and regulators, as the final checkpoint before IFRS 9 use.

Explore cross-validation to test model stability, generalizability, and reduce overfitting, using five-fold cross-validation in Excel with random folds and averaged Gini, AUC, and Brier scores.

Conclude IFRS 9 model validation in Excel with calibrated, discriminative, and stable results; apply best practices like documenting processes, regular monitoring, and translating to other platforms with clear visuals.

Calibrate Pitt PD models to observed defaults and apply forward looking macroeconomic adjustments using at least three scenarios with weighting to refine ECL under IFRS 9.

Learn how management overlays add expert judgment to IFRS 9 expected credit loss modelling, addressing Covid, data gaps, and emerging risks with strong governance.

Explore the three IFRS 9 stages—performing, significant credit risk increase, and credit impaired—and their integration with Basel IRB PD models, lifetime EAD, and LGD adjustments.

Examine how IFRS 9 models faced Covid 19 shocks, with deferrals complicating staging. Showcase how banks used scenarios, overlays, governance, and disclosures to reflect risk in crisis.

Integrate forward-looking information into IFRS 9 credit risk models using macroeconomic forecasting and scenario analysis in Excel, applying base, downside, and upside scenarios with probabilities to adjust PD estimates.

Calibrate pit pd models to reflect current and expected economic conditions under IFRS 9, aligning defaults with real outcomes. Explore forward-looking adjustments and multiple scenarios.

Weight macroeconomic scenarios to produce a probability-weighted PD for IFRS 9, linking GDP, unemployment, rates, and inflation to PD and calibrating predictions with scaling, Platt, or isotonic methods.

Learn calibration for credit risk models using calibration in the large and Platt scaling to adjust the logit, fixing level bias and spread distortion while preserving rankings.

Master calibration and forward looking adjustments with hands-on practice using the provided data set and code scripts to calibrate models and improve credit risk predictions in Excel.

Learn IFRS 9 staging and SICR assessment to assign stage one, two, or three using quantitative and qualitative triggers, including 30-day past due backstop, with SaaS automation.

Bridge the structural Merton framework with IFRS 9 PIT PD modeling in Excel to translate market-implied one year default risk into 12 month and lifetime PD across stages.

Explore IFRS 9 staging and significant increase in credit risk. Learn how Basel overlays adapt IFRS 9 with 12-month vs lifetime ECL, stage transitions, triggers, macro overlays, and validation.

Validate IFRS 9 staging rules to ensure stage allocations reflect credit risk and provide predictive power for expected credit losses, using validation dimensions and KS, Gini, PSI, lift and recall.

Develop and validate IFRS 9 staging with a macro-based framework that assigns loans to stage one, two, or three, and verify using proxy default rates, lift tables, and transition matrices.

Explore IFRS 9 staging and significant credit risk, differentiating 12-month and lifetime expected credit loss, and apply quantitative and qualitative triggers within a SaaS implementation.

Calculate the lifetime ECL under IFRS 9 by applying monthly PD to LGD and EAD, discounting, and summing across months.

Adopt lifetime PD to strengthen credit risk management under IFRS 9 and Basel, improving ECL accuracy, risk-based pricing, capital planning, and early warning systems.

Explore the differences between point in time Pi PD and lifetime PD under IFRS nine, and show how stage one to stage two transitions trigger a cliff effect in provisions.

Explore survival functions in credit risk and their link to probability of default and lifetime PD under IFRS nine, including how survival equals one minus cumulative default.

Track cohorts by origination month to reveal how defaults accumulate as loans age, strengthening IFRS 9 lifetime PD modeling for better forecasting and decisions.

Learn how transition matrices model credit risk by showing how loans move from current to late or default, and how to forecast credit quality under IFRS 9.

Compute 12-month and lifetime ECLs by stage under IFRS 9, aggregating portfolio provisions and reporting with forward-looking PD, LGD, and EAD, discounted at the effective interest rate.

Explain how IFRS 9 ECL aligns with prudential expected loss concepts and map financial statements to pillar three templates, establishing disciplined data lineage, reconciliations, and governance for regulatory reporting.

Master the 12 month expected credit loss under IFRS 9 for stage one assets by applying the PD, LGD, EAD, and DF formula and understanding which instruments qualify.

Explore lifetime expected credit loss under IFRS 9, distinguishing stage two from 12-month ACL, and apply forward-looking, multi-scenario modeling to loans, mortgages, and trade receivables.

Master IFRS 9 impairment in Excel with forward-looking ECL, PD, LGD, and EAD, using the three-stage model to map P&L impact. Explore Basel III and regulatory reconciliation.

Explore IFRS 9 classification pillar: use the business model and SPPI tests to determine amortized cost, FVOCI, or FVTPL, with P&L or OCI outcomes and ECL impairment.

Explore advanced portfolio aggregation techniques for IFRS 9 PIT PD, LGD, and ECL calculations in excel, with stage-based risk, exposure-weighted results, scenario analysis, and concentration insights.

Understand lifetime ECL and its difference from 12-month ECL, and apply the multi-period formula using probability of default, loss given default, exposure at default, discounting, across instruments.

Learn how sensitivity analysis improves transparent ECL reporting under IFRS 9, quantify how assumption changes drive expected credit loss, and blend single-factor, scenario, and weighted-average disclosures.

Learn IFRS 9 regulatory reporting and disclosures for credit risk modeling in Excel, focusing on ECL, PD, LGD, staging, and transparent narratives for regulators, auditors, and investors.

Learn IFRS 9 expected credit loss calculations by combining PD, LGD, and EAD, with 12-month and lifetime estimates, discounting, and scenario analysis.

Automate IFRS 9 PD workflows from data ingestion to model execution and reporting, with monitoring, alerts, and dashboards to ensure data quality, consistency, and governance.

Learn how to implement continuous model validation and monitoring under IFRS 9, ensuring accurate ECL estimates, robust macroeconomic monitoring, governance, and risk management through discrimination, calibration, and backtesting.

Automate IFRS 9 workflows with resilient architecture and pipeline design in Excel, covering data ingestion, model execution, ACL calculations, disclosures, and end-to-end governance.

Move point-in-time PD models from development to a robust automated production environment using SAS macros, batch scoring, and API deployment for IFRS 9 ECL.

Establish robust governance and automated controls across IFRS 9 workflows—from ingestion to reporting—enforcing preventive, detective, and corrective controls, SLAs, audit trails, and structured change management.

Monitor IFRS 9 model performance and data quality across PD, LGD, EAD, and ECL, using segmentation, backtesting, and AUC, Brier score, PSI to ensure robust, auditable outputs.

Configure exception-based alerts for IFRS 9 monitoring, defining green-amber-red thresholds with hysteresis and suppression; route, escalate, and assign ownership via templates, dashboards, and audit trails.

Automates and monitors IFRS 9 credit risk modeling in Excel, enabling repeatable workflows, integrated reporting to PDF and PowerPoint, data quality checks, and transparent governance for regulators.

Integrate all IFRS 9 pd modeling steps into a cohesive automation-ready framework, from data sourcing and model development to calibration, staging, lifetime pd, acl calculation, and real-time monitoring.

Implement a complete IFRS 9 pit PD framework from raw data ingestion to reporting, including staging, lifetime PD, macroeconomic indicators, and ACL calculations.