Credit Risk Modeling
There is a new version of this course. Please see Credit Risk Modeling.
Business Knowledge Series course
In this course, students learn how to develop credit risk models in the context of the recent Basel II and Basel III 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.
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
Formats available  Duration   
eLearning: 
24 hours/180 day license 

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
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
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.)
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 KSstatistic
 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)
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
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 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
Survival Analysis (included only in 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
BB4C132

