3 Days/ 18 hours Workshop Details:

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Module 1 (3 hours)

Time	Topic
80min	Data Pre-processing and Credit Scoring Credit scoring techniques for corporate & retail data Outlier detection Standardisation & categorisation Default definitions Non-monotone relations between defaults and risk factors Weight-of-evidence, categorisation, splines Multivariate non-monotone relationship
20min	Break
80min	Probabilities of Default (PD) — Expected PD Discrete time models (Logit & Probit) Comprehensive modeling including vintage, age and time effects Maximum likelihood estimation Model stability, discrimination & calibration

Module 2 (3 hours)

Time	Topic
80min	Probabilities of Default (PD) – Crisis PDs Through-The-Cycle & Point-In-Time Predicting financial crises Margin of Conservatism (MoC) Stress-testing
20min	Break
80min	Probabilities of Default (PD) - PD Term Structures Roll rates Age splines and macroeconomic predictions Survival models Payoff modeling and survival probabilities Conditional vs. unconditional PDs

Module 3 (3 hours):

Time	Topic
80min	Machine Learning Concepts Statistical and Machine Learning Thinking in Machine Learning Loss Functions Train-Test-Validation-Split Standardisation and Scaling Bias-Variance-Tradeoff Oversampling and Undersampling Cross-Validation Hyper-Parameters and Tuning Supervised and Unsupervised Learning Classification and Regression vs. Dimensionality Reduction
20min	Break
80min	Machine Learning Validation Validation framework Backtesting PDs: Discrimination, Calibration, Stability Confusion matrix, ROC curves, AUROC & accuracy ratio Brier scores Binomial test Jefreys prior test Hosmer-Lemeshow Calibration curve and reliability diagram Tests on Model Stability Portfolio dependence Traffic lights Qualitative validation

Module 4 (3 hours):

Time	Topic
80min	Unsupervised Machine Learning for PDs Bayesian regression techniques and classifiers K Means Clustering Hierarchical Clustering Principal Component Analysis
20min	Break
80min	Supervised Machine Learning for PDs — Standalone Techniques Logistic Regressions K nearest Neighbours Decision Trees Bagging Random Forests

Module 5 (3 hours):

Time	Topic
80min	Supervised Machine Learning for PDs — Boosting Boosting Adaptive Boosting Gradient Boosting Stochastic Gradient Boosting Light GBM
20min	Break
80min	Supervised Machine Learning for PDs - Deep Learning Deep Learning Neural Networks Support Vector Machines

Module 6 (3 hours):

Time	Topic
80min	Machine Learning Concepts for LGD LGD discount rates Computing observed LGD LGD discount rates Marginal modeling (linear, fractional logit & beta regressions) Decision Trees Random Forests Boosted Trees Support Vector Machines
20min	Break
80min	Current expected credit losses (CECL) for US GAAP and IFRS 9 Loan loss provisioning and Basel capital 12-month expected loss vs. lifetime expected loss Rating class formation and roll rate analysis Macroeconomic forecasts Multi-period PD forecasts based on the macroeconomy and lifecycle Prediction of lifetime expected losses Significant increase in credit risk (SICR) Loan pricing Capital models

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