Bart Baesens

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Credit Risk Modeling for Basel/IFRS 9 using R/Python/SAS

📅 May 11-12
🌍 English

About This Course

In this course, students learn how to develop credit risk models in the context of the Basel and IFRS 9 guidelines. The course extensively reviews the 3 key credit risk parameters: Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD). Modeling methods, performance measurement and benchmarks are discussed into great detail. 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 examples. The course also features code examples in R, Python and SAS. Throughout the course, the instructors also extenisvely report upon their research and industry experience.  A certificate signed by the instructors is provided upon successful completion.



Before subscribing to this course, you should have business expertise in credit risk and a basic understanding of descriptive statistics (e.g., mean, median, standard deviation, etc.) and inference (e.g., confidence intervals, hypothesis testing). Previous R, Python and SAS experience is helpful but not necessary.

Course Outline

  • Introduction
    • Instructor team
    • Our Credit Risk Publications
    • Software
    • R/Python tutorials
    • Data sets
    • Disclaimer
  • Credit scoring
    • Introduction
    • Retail credit scoring
    • Application scoring
    • Behavioral scoring
    • Corporate credit scoring
    • Prediction approach
    • Expert-based approach
    • Agency Rating approach
    • Shadow Ratings approach
    • Quiz
    • Discussion
  • Basel Accords/IFRS 9/CECL
    • Regulatory versus Economic Capital
    • Basel I and II Capital Accords
    • Basel III
    • Basel IV
    • Basel approaches to model credit risk
    • Standardized approach
    • IRB approach
    • Default rating specifics
    • Basel IRB model architecture
    • Risk weight functions for retail
    • Merton model
    • Risk weight functions
    • IFRS 9
    • Credit risk model lifecycle
    • Quiz
    • Discussion
  • Data Preprocessing
    • Motivation
    • Types of data
    • Types of variables
    • Denormalizing data
    • Sampling
    • Visual data exploration
    • Descriptive statistics
    • Missing values
    • Outliers
    • Categorization
    • WOE and IV
    • Variable Transformation
    • Quiz
  • Classification Techniques
    • Linear Regression
    • Logistic Regression
    • Nomograms
    • Decision trees
    • Cumulative logistic regression
    • Multiclass decision trees
    • Quiz
  • Measuring the performance of credit scoring classification models
    • Split sample method
    • Cross-validation
    • Single sample method
    • Confusion matrix (classification accuracy, classification error, sensitivity, specificity)
    • ROC curve and area under ROC curve
    • CAP curve and Accuracy Ratio
    • Lift curve
    • Kolmogorov-Smirnov distance
    • Mahalanobis distance
    • Performance benchmarks
    • Multiclass confusion matrix
    • Notch difference graph
    • Multiclass AUC
    • Quiz
  • Survival analysis
    • Censoring
    • Time varying covariates
    • Survival distributions
    • Kaplan Meier analysis
    • Accelerated Failure Time models
    • Proportional hazards model
    • Discrete survival analysis
    • Competing risks
    • Mixture cure modeling
    • Survival analysis in credit risk modeling
    • Evaluating survival analysis
    • Quiz
  • Defining default ratings and calibrating PD
    • Defining default ratings
    • Rating migration analysis
    • Rating philosophy
    • Rating mobility
    • PD calibration
    • Quiz
  • Modeling Loss Given Default (LGD)
    • Definition of default
    • Definition of LGD
    • Ways of measuring LGD
    • LGD according to Basel
    • Constructing an LGD data set
    • Complete business Cycle
    • Default definition and cures
    • LGD measurement
    • Length of workout period
    • Incomplete workouts
    • Discount rate
    • LGD < 0 or LGD>100%
    • Indirect costs
    • LGD drivers
    • Data preprocessing
    • Challenges in LGD modeling
    • LGD modeling approaches
    • Segmentation
    • Regression trees
    • Linear regression LGD modeling
    • Beta regression LGD modeling
    • Logistic regression LGD modeling
    • Cumulative logistic regression LGD modeling
    • Two stage LGD models
    • Advanced LGD models
    • Performance measures
    • LGD ratings
    • LGD calibration
    • Exposures in default
  • Modeling Exposure At Default (EAD)
    • Defining Exposure At Default (EAD)
    • EAD according to Basel
    • Drawings post default
    • EAD Development Sample
    • CCF < 0 or CCF>100%
    • EAD modeling
    • EAD Case Study
    • Correlation between PD/LGD/EAD
👩‍🏫 Lecturers

Prof. dr. Wouter Verbeke
Professor at KU Leuven

Prof. dr. Bart Baesens
Professor at KU Leuven

🏢 Location

The Fourth Hotel Leuven

Grote Markt 5, 3000 Leuven, Belgium

🏫 Organizer

Bart Baesens

💼 Register

The price is 900 Euro (VAT Exclusive) for both days. This includes:

  • a copy of the course material (including Python/R/SAS  code, and toy credit risk data sets)
  • lunches and coffees

Online registration

Price and Registration

The price is 900 Euro (VAT Exclusive) for both days. This includes:

Please register through the link below. After processing your payment, you'll be sent a confirmation e-mail to confirm your registration.

Online registration