Bart Baesens

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Credit Risk Modeling (E-learning)

📅 Self-Paced E-learning course
🌍 English


Business Knowledge Series course

Presented by Bart Baesens, Ph.D. Professor at the School of Management of the University of Southampton (UK); or Christophe Mues, Ph.D., Professor at the School of Management of the University of Southampton (UK); or Cristian Bravo, Ph.D, Assistant Professor, Business Analytics, University of Southampton (UK); or Wouter Verbeke, Ph.D., Assistant Professor, Business Informatics, University of Brussels (Belgium); or Stefan Lessmann, Ph.D., Professor, School of Business and Economics, Humboldt University (Germany)

The E-learning course covers both the basic as well some more advanced ways of modeling, validating and stress testing Probability of Default (PD), Loss Given Default (LGD ) and Exposure At Default (EAD) models.  Throughout the course, we extensively refer to our industry and research experience. Various business examples and small case studies in both retail and corporate credit are also included for further clarification.  The E-learning course consists of more than 20 hours of movies, each 5 minutes on average.  Quizzes are included to facilitate the understanding of the material. Upon registration, you will get an access code which gives you unlimited access to all course material (movies, quizzes, scripts, ...) during 6 months. The course focusses on the concepts and modeling methodologies and not on the SAS software.  To access the course material, you only need a laptop, iPad, iPhone with a web browser. No SAS software is needed.

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.

Course Outline

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, z-scores, truncation, etc.)
  • exploratory data analysis
  • categorization (chi-squared 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, 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, etc,)
  • 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 4-day 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 4-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
👩‍🏫 Lecturers

Prof. dr. Bart Baesens
Professor at KU Leuven

🏢 Location

Anywhere (e-learning).

🏫 Organizer


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