Day 1:
Duration |
Topic |
80 min |
PD/LGD principles
Transitional (climate and geopolitical) risks
Impact of COVID-19, income and payment shocks on credit risk
|
20 min |
Break with industry speaker |
80 min |
You will join a breakout in Python or R language to apply: Key Programming Concepts
Visualisation and Reporting through Dashboards
|
Day 2:
Duration |
Topic |
80 min |
Machine Learning Concepts for Probabilities of Default (PDs)
Machine Learning Validation for PDs
|
20 min |
Break with industry speaker |
80 min |
You will join a breakout in Python or R language to apply: Estimate a comprehensive PD model
Validate a comprehensive PD model
|
Day 3:
Duration |
Topic |
80 min |
Crisis PDs
Downturn LGD based on Workout Recoveries
Multi-period PDs and Lifetime PDs for CECL and IFRS 9
|
20 min |
Break with industry speaker |
80 min |
You will join a breakout in Python or R language to apply: Prediction of Crisis PDs
Prediction of LGDs Expected and unexpected loss modeling for IFRS 9
Unexpected loss modeling for capital
|
Day 4:
Duration |
Topic |
80 min |
Supervised Machine Learning for PDs
Explainable AI
|
20 min |
Break with industry speaker |
80 min |
You will join a breakout in Python or R language to apply: Fit Machine Learning Models
Do it like Google: Introduction into Keras/Tenserflow
|