Credit Risk Modeling
Business Knowledge Series course
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 reallife 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.
Learn how to
 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.
Who should attend Anyone who is involved in building credit risk models or is responsible for monitoring the behavior and performance of credit risk models
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.
This course addresses SAS Enterprise Miner software.
Introduction to Credit Scoring application scoring, behavioral scoring, and dynamic scoring
 credit bureaus
 bankruptcy prediction models
 expert models
 credit ratings and rating agencies
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
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, zscores, truncation, etc.)
 exploratory data analysis
 categorization (chisquared analysis, odds plots, etc.)
 weight of evidence (WOE) coding and information value (IV)
 segmentation
 reject inference (hard cutoff augmentation, parceling, etc.)
Sampling and Data Preprocessing Selecting the sample.
 Types of variables.
 Missing values (imputation schemes).
 Outlier detection and treatment (box plots, zscores, truncation, and so on).
 Exploratory data analysis.
 Categorization (chisquared 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, knearest neighbor, cumulative logistic regression.
 Input selection methods such as filters, forward/backward/stepwise regression, and pvalues.
 Setting the cutoff (strategy curve, marginal goodbad rates).
 Measuring scorecard performance.
 Splitting up the data: single sample, holdout sample, crossvalidation.
 Performance metrics such as ROC curve, CAP curve, and KS statistic.
 Defining ratings.
 Migration matrices.
 Rating philosophy (PointinTime versus ThroughtheCycle).
 Mobility metrics.
 PD calibration.
 Scorecard alignment and implementation.
Developing PD Models basic concepts of classification
 classification techniques: logistic regression, decision trees, linear programming, knearest neighbor, cumulative logistic regression
 input selection methods such as filters, forward/backward/stepwise regression, and pvalues
 setting the cutoff (strategy curve, marginal goodbad rates)
 measuring scorecard performance
 splitting up the data: single sample, holdout sample, crossvalidation
 performance metrics such as ROC curve, CAP curve, and KS statistic
 defining ratings
 migration matrices
 rating philosophy (PointinTime versus ThroughtheCycle)
 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 twostage 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).
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 twostage 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 chisquared tests.
 Traffic light indicator approach.
 Backtesting action plans.
 Throughthecycle (TTC) versus pointintime (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.
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 chisquared tests
 traffic light indicator approach
 backtesting action plans
 throughthecycle (TTC) versus pointintime (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
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.
 Macroeconomic stress testing.
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
 macroeconomic stress testing
Neural Networks (included only in 4day classroom version) background
 the 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
Neural Networks (included only in fourday 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 4day classroom version) survival analysis for credit scoring
 basic concepts
 censoring
 timevarying covariates
 survival distributions
 KaplanMeier analysis
 parametric survival analysis
 proportional hazards regression
 discrete survival analysis
 evaluating survival analysis models
 competing risks
 mixture cure modeling
Survival Analysis (included only in fourday classroom version) Survival analysis for credit scoring.
 Basic concepts.
 Censoring.
 Timevarying covariates.
 Survival distributions.
 KaplanMeier analysis.
 Parametric survival analysis.
 Proportional hazards regression.
 Discrete survival analysis.
 Evaluating survival analysis models.
 Competing risks.
 Mixture cure modeling.
BB4C42

