Module 1: Credit Risk Internal Models & Model Risk in Machine Learning Exercises / Labs: Exercise 1: Map EU regulations to internal model approval steps Exercise 2: Build a model risk framework checklist Exercise 3: Extend an LGD model with climate-related variables Lab 1: Train an ML PD model in Python and apply SHAP for explainability Exercise 4: Create an IRB model governance flowchart Module 2: Model Risk Process Exercises / Case Studies: Case Study 1: Model risk management for credit risk models – validation and documentation review Module 3: Model Risk Management Directive SR 11-7 (USA) (No specific exercises listed) Module 4: Guide for the Targeted Review of Internal Models (TRIM) – EU (No specific exercises listed) Module 5: Validation of Models in Practice (No specific exercises listed) Module 6: Model Risk Management – Governance & Lifecycle Exercises / Case Studies: Case Study 2: Scorecard for model risk Module 7: Quantitative Measurement of Model Risk (No specific exercises listed) Module 8: Models for Quantifying Model Risk Exercises: Exercise 1: Estimation of model risk scorecard – definition of scale and ranges Exercise 2: Estimation of model risk using the following frequency and severity distributions: Frequency: Poisson, Negative Binomial Severity: Lognormal, Gamma, Weibull, Inverse Gaussian, GDP EVT, G-H (4 parameters), Mixture of Lognormals, Lognormal-EVT, Poisson-Gamma (Bayesian), Lognormal & GDP partition, Expert-based scenarios Exercise 3: Selection of best distribution using Cramer–von Mises, Anderson–Darling, and Kolmogorov–Smirnov tests Exercise 4: Comparison of model risk capital using Recursive Panjer, FFT and Monte Carlo Simulation Module 9: Model Risk in Credit Scoring (No specific exercises listed) Module 10: Exploratory Data Analysis (EDA) and Binning Exercises: Exercise 5: Exploratory data analysis in R Exercise 6: Detection and treatment of outliers in R Exercise 7: Missing value imputation techniques in R Exercise 8: Stratified and random sampling Exercise 9: Weight of Evidence (WoE) analysis in Excel Exercise 10: Univariate analysis in percentiles in R Exercise 11: Optimal univariate analysis for continuous variables in R Exercise 12: KS, Gini and Information Value validation for each variable Exercise 14: Optimization of categorical variables in R Exercise 15: Univariate analysis with decision trees in R Exercise 16: Segmentation using K-means clustering in R Module 11: Machine Learning Models (Unsupervised, Ensemble Learning – no exercises explicitly listed) Module 12: Deep Learning (NN, CNN, RNN, GANs) (No explicit exercises in this block) Module 15: Generative AI Exercises: Exercise 17: Embeddings for words, sentences, Q&A Exercise 18: Embeddings on a large dataset Exercise 19: Advanced prompting techniques Exercise 20: Large Language Models in Credit Rating Module 16: Multivariate Models and Machine Learning – Model Risk Exercises: Exercise 21: Logistic regression (stepwise) in R Exercise 22: Piecewise regression in Excel and R Exercise 23: Decision trees in R Exercise 24: Support Vector Machine in R Exercise 25: Neural networks in R Exercise 26: Ensemble models in R Exercise 27: Random Forest in R Exercise 28: Bagging in R Exercise 29: Credit scoring using Deep Learning CNN (Python) Exercise 30: Credit scoring using Deep Learning LSTM (Python) Exercise 31: Credit scoring using GANs (Python) Exercise 32: Comparison of discriminant power between models (NN, Logistic Regression, Panel Logistic Regression, Cox Regression) Exercise 33: Model risk using confidence intervals of logistic regression coefficients Module 14: Tuning Hyperparameters in Deep Learning Exercises: Exercise 34: Tuning hyperparameters in XGBoosting, Random Forest and SVM for credit scoring Exercise 35: Tuning hyperparameters in a deep learning model for credit scoring Module 17: Model Risk in the Scorecard Exercises: Exercise 36: Construction of a scorecard in Excel Exercise 37: Optimal cut-off point estimation in Excel and model risk by cut-off choice Exercise 38: Confusion matrix to verify Type I and Type II errors in Excel (with and without variables) Exercise 39: Model risk in credit scoring due to lack of timely recalibration Module 18: Stability Tests Exercises: Exercise 40: Stability tests of models and factors Module 19: Validation of Traditional and Machine Learning Models Exercises: Exercise 41: Cross-validation in R Exercise 42: Gini, Information Value, Brier Score, Lift Curve, CAP, ROC, Divergence in Excel Exercise 43: Bootstrapping parameters in R Exercise 44: Gini/ROC bootstrapping in R Exercise 45: Kappa statistic estimation Exercise 46: K-Fold Cross Validation in R Exercise 47: Out-of-time traffic-light validation (6-year horizon) for Logistic and ML models Module 20: Explainable Artificial Intelligence (XAI) Exercises: Exercise 48: XAI interpretability of credit scoring Module 21: Advanced Validation of AI Models Exercises: Exercise 49: Validation and diagnosis of advanced credit scoring models Module 22: Directive on PD and LGD Estimation (EBA / ECB Guide July 2025) (No specific exercises listed) Module 23: PD Templates for IRB and IFRS 9 Exercises: Exercise 50: PD calibration with Cox regression in R Exercise 51: PD calibration with log-log complementary regression in R Exercise 52: PD calibration with logistic regression in R Exercise 53: PD calibration with Bayesian logistic regression Exercise 54: PD calibration with panel logistic regression Exercise 55: PD calibration with Lasso regression Exercise 56: PD calibration with Bayesian probit regression in R Exercise 57: Transition matrices in Excel and R Exercise 58: Multinomial regression to estimate PD Lifetime Exercise 59: Multi-stage Markov chains in R Module 24: Backtesting PD IRB and IFRS 9 Exercises: Exercise 60: Backtesting PD IRB and PD IFRS 9 Exercise 61: Forecasting estimated PD vs real PD in Excel Exercise 62: Validation using Monte Carlo Simulation Module 25: LGD Models for IRB and IFRS 9 Exercises: Exercise 63: Logistic and linear regression LGD in R Exercise 64: Neural Networks and SVM for LGD Exercise 65: Generalized Additive Model LGD in R Exercise 66: Beta Regression Model LGD in R Exercise 67: Long-term LGD calibration Exercise 68: Inflated Beta Regression in SAS Exercise 69: Comparison of model performance using calibration and accuracy tests Module 26: LGD Backtesting Exercises: Exercise 70: Comparison of performance of LGD models using calibration and accuracy tests Module 27: EAD Models Exercises: Exercise 56: Estimation and adjustments for EAD IFRS 9 in Excel and R Exercise 57: Neural Networks and SVM for CCF Exercise 58: Generalized Additive Model CCF in R Exercise 59: Beta Regression Model CCF in R Module 28: EAD Backtesting Exercises: Exercise 71: Comparison of performance of EAD models Module 29: ECL Validation (IFRS 9) (No explicit exercises listed) Module 30: Economic Capital Models Exercises: Exercise 72: Default correlation matrix in SAS Exercise 73: Default correlation for consumer portfolios in R Exercise 74: Asset correlation with EMV and observable data in R Exercise 75: CreditRisk+ in R Exercise 76: One-factor model in Excel and R Exercise 77: Multifactorial model in Excel Exercise 78: t-Student copula in Excel Exercise 79: Copulas in R Module 31: Economic Capital Validation Exercises: Exercise 80: Implementation of Berkowitz test in economic and regulatory capital models Exercise 81: Simulation of losses and model risk in regulatory and economic capital Module 32: Forecasting Models for Stress Testing Validation Exercises: Exercise 82: Non-stationary series and cointegration tests in R and Python Exercise 83: Macroeconomic variables with VAR in R Exercise 84: GARCH modelling of market variables in R Exercise 85: Machine Learning SVM and NN modelling in Python Module 33: Validation of Econometric Models Exercises: Exercise 86: Measuring logistic regression multicollinearity Module 34: Stress Testing Consumer Credit Risk Exercises: Exercise 87: Stress testing PD in Excel and R – multifactorial model Exercise 88: Stress testing PD – Multiyear Approach Exercise 89: Stress test of PD and autoregressive vectors Exercise 90: Net Charge-Off stress test Exercise 91: LGD stress test Module 35: Stress Testing Credit Risk Corporate Portfolios Exercises: Exercise 92: Corporate portfolio stress test using transition matrices Module 36: Stress Testing of ECL IFRS 9 Exercises: Exercise 93: Stress testing ECL using matrices and time series in R and Excel Module 37: Validation of Stress Testing Exercises: Exercise 94: Validation tests of stress testing VAR vs MARS Module 38: Build Automation and Calibration (AI Automation for Modeling) Exercises: Exercise 95: Automation of modeling, optimization and hyperparameter validation for credit scoring Module 40: Expanded Module – Agent AI for Model Validation Exercises / Project: Practical Exercise 96 (Project): Build a prototype AI Agent for Validation Monitor: calculate AUC, PSI, KS monthly Analyse: SHAP explainability for ML model Validate: compare calibration (predicted PD vs actual defaults) Report: automated validation summary (traffic-light dashboard + report)

Module 1: Introduction to Model Risk (No exercises) Module 2: Model Risk Management (No exercises) Module 3: Model Risk Management Guidelines – SR 11-7 (US) (No exercises) Module 4: Guide for the Targeted Review of Internal Models (TRIM – EU) (No exercises) Module 5: Quantitative Measurement of Model Risk (No exercises) Module 6: Models to Quantify Model Risk Exercise 1: Estimation of a model risk scorecard, definition of scale and ranges. Exercise 2: Estimation of model risk using frequency and severity distributions (Poisson, Negative Binomial; Lognormal, Burr, Gamma, Weibull, Inverse Gaussian, GDP EVT, 4-parameter g-and-h, lognormal mixtures, lognormal–EVT, Poisson–Gamma Bayesian, lognormal–GDP partition, expert-based scenarios). Exercise 3: Selection of the best distribution using goodness-of-fit tests (Cramér–von Mises, Anderson–Darling, Kolmogorov–Smirnov). Exercise 4: Comparison of model risk using Panjer recursion, Fast Fourier Transform and Monte Carlo simulation. Module 7: Advanced Data Validation Exercise 5: Exploratory data analysis in R. Exercise 6: Detection and treatment of outliers in R. Exercise 7: Missing value imputation techniques in R. Exercise 8: Stratified and random sampling. Exercise 9: Weight of Evidence (WOE) analysis in Excel. Exercise 10: Univariate percentile analysis in R. Exercise 11: Optimal univariate analysis for continuous variables in R. Exercise 12: KS, Gini and Information Value validation for each variable in R and Excel. Exercise 14: Optimization of categorical variables in R. Exercise 15: Univariate analysis with decision trees in R. Exercise 16: Segmentation using K-means clustering in R. Module 8: Multivariate Models and Machine Learning Exercise 17: Logistic regression with stepwise selection in R. Exercise 18: Piecewise regression in Excel and R. Exercise 19: Decision trees in R. Exercise 20: Support Vector Machine in R. Exercise 21: Neural networks in R. Exercise 22: Ensemble models in R. Exercise 23: Random Forest in R. Exercise 24: Bagging in R. Exercise 26: Comparison of discriminant power between neural networks and logistic regression models. Exercise 27: Model risk using confidence intervals of logistic regression coefficients. Module 9: Model Risk in the Scorecard Exercise 28: Construction of a scorecard in Excel. Exercise 29: Optimal cut-off point estimation in Excel and model risk from cut-off selection. Exercise 30: Confusion matrix to verify Type I and Type II error in Excel with and without variables. Module 10: Stability Tests Exercise 31: Stability tests for models and factors. Module 11: Discriminant Power of Traditional and Machine Learning Models Exercise 32: Estimation of Gini, Information Value, Brier Score, Lift curve, CAP, ROC and divergence in SAS and Excel. Exercise 33: Jackknifing. Exercise 34: Bootstrapping. Exercise 35: Kappa statistic estimation. Exercise 36: K-fold cross-validation in R. Exercise 37: Traffic-light (semaphore) validation out-of-time (6-year horizon) for logistic and ML models. Module 12: Model Risk in Credit Scoring Exercise 38: Application of a credit scoring tool over a 2-year horizon without updating data. Exercise 39: Measurement of model risk via Type I/II errors and losses from accepting bads and opportunity cost from rejecting goods. Module 14: Explainable Artificial Intelligence (XAI) Exercise 40: XAI-based interpretability of credit scoring. Module 15: Automation of Modelling Exercise 41: Automation of credit scoring modelling, hyperparameter optimization and validation. Module 16: PD Calibration and Backtesting Exercise 42: PD calibration using central tendency adjustment. Exercise 43: PD calibration using quasi-moment matching. Exercise 44: PD calibration using regression approaches. Exercise 45: PD calibration in machine learning models. Exercise 46: PD backtesting in Excel. Exercise 47: Forecasting PD and realized PD in Excel. Exercise 48: Validation using Monte Carlo simulation. Module 17: Alternative PD Backtesting Exercise 49: PD validation using a latent-variable model. Module 18: LGD Calibration and Backtesting Exercise 50: Model performance comparison using calibration and accuracy tests. Module 19: EAD Backtesting Exercise 51: Performance comparison of EAD models. Module 20: Lifetime PD Estimation and Backtesting (IFRS 9) Exercise 52: Machine learning to estimate Lifetime PD. Exercise 53: Multinomial regression to estimate Lifetime PD. Exercise 54: Cox regression to estimate Lifetime PD. Exercise 55: Multi-stage Markov chains in R. Exercise 56: Stability and accuracy testing of Lifetime PD. Exercise 57: Lifetime PD calibration for machine learning models. Module 21: Estimation and Validation of Expected Credit Losses (ECL) – IFRS 9 Global Exercise 58: Lifetime ECL estimation (Lifetime PD, LGD, EAD, prepayment probability and scenarios). Global Exercise 59: Validation through ECL simulation and economic capital. Module 22: IFRS 9 ECL Validation (No exercises) Module 23: Economic Capital Models Exercise 60: Default correlation in retail/consumer portfolios. Exercise 61: Asset correlation with EMV (e.g., Exogenous Maturity Vintage). Exercise 62: Sectoral CreditRisk+ implementation. Exercise 63: Single-factor model. Exercise 64: Multi-factor model. Exercise 65: Copulas in R. Module 24: Validation and Model Risk in Economic Capital Exercise 66: Capital validation using the Berkowitz test. Exercise 67: Model risk due to uncertainty in CreditRisk+. Exercise 68: Model risk due to data deficiencies in CreditRisk+. Module 25: Model Risk – Parameter Uncertainty in Credit Risk Exercise 69: Model risk measurement in an ASRF credit risk model (parameter uncertainty in credit scoring, PD and LGD calibration). Exercise 70: Model risk of VaR and ES under parametric lognormal distribution. Exercise 71: Model risk from PD and asset-correlation parameter uncertainty using a Bayesian approach. Exercise 72: Propagation of uncertainty in credit risk models. Module 26: Statistical Validation of Models Exercise 73: Comprehensive analysis of regression statistical tests. Module 27: Estimation and Validation of Financial & Macroeconomic Time Series Models Exercise 74: Non-stationary and cointegrated series analysis. Exercise 75: GARCH modelling of market variables. Exercise 76: Machine learning modelling (SVM and neural networks). Exercise 77: Backtesting of machine learning time series models. Module 28: Determination of Macroeconomic Scenarios in IFRS 9 Exercise 78: Advanced macroeconomic scenario modelling with BVaR and DSGE. Module 29: Stress Testing Net Charge-Off Exercise 79: VAR stress testing model. Exercise 80: VEC stress testing model. Exercise 81: MARS stress testing model. Module 30: Stress Testing Validation Exercise 82: Validation tests comparing VAR vs MARS stress testing models. Module 31: Model Risk in IFRS 9 Stress Testing Exercise 83: Model risk in stress testing using the Credit Portfolio View approach. Exercise 84: Model risk in the Multiyear approach. Exercise 85: Model risk in LGD stress testing. Exercise 86: Model risk in transition matrix stress testing. Module 32: Margin of Conservatism (MoC) – IRB Exercise 87: Estimation of MoC for PD, LGD and EAD under two different methodologies.