Training course on Credit Risk Modeling

Module 1: Introduction to Credit Risk Modeling

The role of credit risk in banking and financial stability

Types of credit risk: borrower, counterparty, concentration

Overview of Basel and IFRS 9 requirements

Module 2: Fundamentals of Probability and Statistics for Credit Risk

Probability distributions in credit risk

Statistical inference and hypothesis testing

Regression and classification basics

Module 3: Credit Scoring and Rating Models

Logistic regression for credit scoring

Discriminant analysis and classification trees

Credit rating migration and transition matrices

Module 4: Probability of Default (PD) Estimation

Point-in-time vs through-the-cycle PDs

Survival analysis and hazard models

Machine learning approaches to PD modeling

Module 5: Loss Given Default (LGD) and Exposure at Default (EAD)

Estimating LGD with recovery rate models

Modeling EAD for different product types (loans, credit lines, derivatives)

Correlation between PD, LGD, and EAD

Module 6: Portfolio Credit Risk Models

CreditMetrics framework

CreditRisk+ and actuarial approaches

KMV and structural models of default

Module 7: Stress Testing and Scenario Analysis

Macro stress testing for credit portfolios

Linking credit risk models to macroeconomic variables

Reverse stress testing

Module 8: Model Validation and Back-Testing

Performance metrics (ROC, AUC, KS statistic, Gini coefficient)

Out-of-sample testing and benchmarking

Model risk management practices

Module 9: Machine Learning and AI in Credit Risk Modeling

Random forests, gradient boosting, neural networks

Feature engineering and variable selection

Explainable AI and regulatory acceptance

Module 10: Credit Risk Modeling for Regulatory Compliance

Basel III/IV capital requirements for credit risk

IFRS 9 Expected Credit Loss (ECL) framework

Supervisory expectations for model governance

Module 11: Case Studies and Practical Applications

Credit risk modeling failures and lessons learned

Building a full credit risk model step-by-step

Real-world case study from banking practice

Module 12: Capstone Project and Hands-On Workshop

Developing PD, LGD, and EAD models using software (e.g., R, Python, SAS)

Stress testing a sample loan portfolio

Presenting model results and recommendations