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3 Days/ 18 hours Workshop Details:


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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  • WELCOME
    • CONTENTS
    • START Python
    • START R
    • FEATURED
  • DATA & CODE
  • TRAINING
    • ONLINE
    • ZOOM MASTERCLASS
    • IN HOUSE
    • UNI
  • PAPERS
  • CONTACT
  • 中文