Credit Risk Modeling

Course code: BB4C42

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.

3 015 EUR

3 648 EUR including VAT

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Starting date: Upon request

Type: Upon request

Course duration: 28 hours

Language: en

Price without VAT: 3 015 EUR


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Target group

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

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.


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.

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