Credit Risk Modeling

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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.