Credit Risk Modeling

Master Class formation

In this course, students learn how to develop credit risk models in the context of the Basel 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.

Please note: This course is not intended to teach credit risk modeling using SAS. Previous SAS software and SAS Enterprise Miner experience is helpful but not necessary.

Apprendre à

• Develop probability of default (PD), loss given default (LGD), and exposure at default (EAD) models.
• Validate, backtest, and benchmark credit risk models.
• Stress test credit risk models.
• Develop credit risk models for low default portfolios.
• Use new and advanced techniques for improved credit risk modeling.

A qui s’adresse cette formation ?

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

Formats de formation disponibles	Durée standard (peut varier selon les sessions. Se référer au calendrier)
Formations à distance:	8 sessions d'une demi-journée chacune
e-Learning:	24 heures/180 jour licence

Before attending this course, you should have business expertise in credit risk and a basic understanding of statistical classification methods. Previous SAS software and SAS Enterprise Miner experience is helpful but not necessary.

Cette formation concerne SAS Enterprise Miner logiciel

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.