In this e-learning course, students learn how to develop credit risk models in the context of the recent Basel II and Basel III guidelines. The course provides a sound mix of both theoretical and technical insights, as well as practical implementation details. These are illustrated by several real-life case studies and exercises. Although the course focusses on the concepts and modelling methodologies, it also includes software demonstrations in Base SAS and SAS Enterprise Miner.

- develop probability of default (PD), loss given default (LGD), and exposure at default (EAD) models
- validate, backtest, and benchmark credit risk models
- develop credit risk models for low default portfolios
- stress test credit risk models
- use new and advanced techniques for improved credit risk modeling

**Prix total ***

Membres: € 1.650

Non-membres: € 1.800

Partenaire BZB: € 1.650

Incompany: sur mesure, prix à la demande

* Prix : prestation dans le cadre du recyclage régulier, exonérée de TVA

Anyone who is involved in building credit risk models, or is responsible for monitoring the behavior and performance of credit risk models.

**Expert level:** Before attending this course, you should have business expertise in credit risk and a basic understanding of descriptive statistics. Previous SAS software and SAS Enterprise Miner experience is helpful but not necessary.

**Introduction to Credit Scoring **

- application scoring, behavioral scoring, and dynamic scoring
- credit bureaus
- bankruptcy prediction models
- expert models
- credit ratings and rating agencies

**Review of Basel I, Basel II, and Basel III **

- Regulatory versus Economic capital
- Basel I, Basel II, and Basel III regulations
- standard approach versus IRB approaches for credit risk
- PD versus LGD versus EAD
- expected loss versus unexpected loss
- the Merton/Vasicek model

**Sampling and Data Preprocessing **

- selecting the sample
- types of variables
- missing values (imputation schemes)
- outlier detection and treatment (box plots, z-scores, truncation, etc.)
- exploratory data analysis
- categorization (chi-squared analysis, odds plots, etc.)
- weight of evidence (WOE) coding and information value (IV)
- segmentation
- reject inference (hard cut-off augmentation, parceling, etc.)

**Developing PD Models **

- basic concepts of classification
- classification techniques: logistic regression, decision trees, linear programming, k-nearest neighbor, cumulative logistic regression
- input selection methods, such as filters, forward/backward/stepwise regression, and p-values
- setting the cut-off (strategy curve, marginal good-bad rates)
- measuring scorecard performance
- splitting up the data: single sample, holdout sample, cross-validation
- performance metrics, such as ROC curve, CAP curve, and KS-statistic
- defining ratings
- migration matrices
- rating philosophy (Point-in-Time versus Through-the-Cycle)
- mobility metrics
- PD calibration
- scorecard alignment and implementation

**Developing LGD and EAD Models **

- modeling loss given default (LGD)
- defining LGD using market approach and work-out approach
- choosing the workout period
- dealing with incomplete workouts
- setting the discount factor
- calculating indirect costs
- drivers of LGD
- modeling LGD
- modeling LGD using segmentation (expert based versus regression trees)
- modeling LGD using linear regression
- shaping the Beta distribution for LGD
- modeling LGD using two-stage models
- measuring performance of LGD models
- defining LGD ratings
- calibrating LGD
- default weighted versus exposure weighted versus time weighted LGD
- economic downturn LGD
- modeling exposure at default (EAD): estimating credit conversion factors (CCF)
- defining CCF
- cohort/fixed time horizon/momentum approach for CCF
- risk drivers for CCF
- modeling CCF using segmentation and regression approaches
- CAP curves for LGD and CCF
- correlations between PD, LGD, and EAD
- calculating expected loss (EL)

**Validation, Backtesting, and Stress Testing **

- validating PD, LGD, and EAD models
- quantitative versus qualitative validation
- backtesting for PD, LGD, and EAD
- backtesting model stability (system stability index)
- backtesting model discrimination (ROC, CAP, overrides, etc.)
- backtesting model calibration using the binomial, Vasicek, and chi-squared tests
- traffic light indicator approach
- backtesting action plans
- through-the-cycle (TTC) versus point-in-time (PIT) validation
- benchmarking
- internal versus external benchmarking
- Kendall's tau and Kruskal's gamma for benchmarking
- use testing
- data quality
- documentation
- corporate governance and management oversight

**Low Default Portfolios (LDPs) **

- definition of LDP
- sampling approaches (undersampling versus oversampling)
- likelihood approaches
- calibration for LDPs

**Stress Testing for PD, LGD, and EAD Models **

- overview of stress testing regulation
- sensitivity analysis
- scenario analysis (historical versus hypothetical)
- examples from industry
- Pillar 1 versus Pillar 2 stress testing
- macro-economic stress testing

The E-learning course consists of about 20 hours of videos. After registration, you can watch the videos as many times as you like during 6 months. You only need a laptop, iPad, etc. with a web browser. No SAS software is needed. The course includes interactive quizzes to facilitate the learning. Upon completion, a certificate will be provided.

**Extra information:** This training will be given in English!

You can follow this training through e-learning in English. You will access the online learning platform as soon as we receive your subscription payment.

**Form:** E-learning

**Teaching Material:** Online training platform

The E-learning course consists of about 20 hours of videos. After registration, you can watch the videos as many times as you like during 6 months. You only need a laptop, iPad, etc. with a web browser. No SAS software is needed. The course includes interactive quizzes to facilitate the learning. Upon completion, a certificate will be provided.