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1 Day/ 8 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 PDs
  • 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
Machine Learning Concepts
  • Statistical and Machine Learning thinking
  • 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 3 (2 hours):
Time
Topic
120min
Supervised Machine Learning for PDs — Standalone Techniques
  • Logistic regression
  • K nearest neighbours
  • Decision trees
  • Bagging
  • Random forests
  • Boosting
  • Adaptive Boosting
  • Gradient Boosting
  • Stochastic Gradient Boosting
  • Light GBM
  • Neural Networks

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