Course structure
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.
- 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, and so on).
- Exploratory data analysis.
- Categorization (chi-squared analysis, odds plots, and so on).
- Weight of evidence (WOE) coding and information value (IV).
- Segmentation.
- Reject inference (hard cutoff augmentation, parceling, and so on).
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 cutoff (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 workout 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, and so on).
- 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.
Neural Networks (included only in four-day classroom version)
- Background.
- Multilayer perceptron (MLP).
- Transfer functions.
- Data preprocessing.
- Weight learning.
- Overfitting.
- Architecture selection.
- Opening the black box.
- Using MLPs in credit risk modeling.
- Self Organizing Maps (SOMs).
- Using SOMs in credit risk modeling.
Survival Analysis (included only in four-day classroom version)
- Survival analysis for credit scoring.
- Basic concepts.
- Censoring.
- Time-varying covariates.
- Survival distributions.
- Kaplan-Meier analysis.
- Parametric survival analysis.
- Proportional hazards regression.
- Discrete survival analysis.
- Evaluating survival analysis models.
- Competing risks.
- Mixture cure modeling.
