Prospective students with additional questions can contact the instructor directly at Bart.Baesens@econ.kuleuven.be.

• the Basel I and Basel II regulation
• standard approach versus IRB approach for credit risk
• PD versus LGD versus EAD
• the Merton/Vasicek model for calculating the regulatory capital
• application scoring, behavioral scoring, and profit scoring
• bankruptcy prediction models

• selecting the sample
• segmentation
• reject inference
• exploratory data analysis
• outlier detection and treatment
• missing values
• weight of evidence coding and information value

• basic concepts of classification
• classification techniques: logistic regression, decision trees, linear programming, k-nearest neighbor, cumulative logistic regression
• input selection, such as filters, stepwise regression, and p-values
• setting the cut-off
• measuring scorecard performance
• performance metrics, such as ROC curve, CAP curve, and KS-statistic
• defining ratings
• scorecard implementation

• modeling loss given default (LGD)
• defining LGD, such as market approach and work-out approach
• time weighted versus default weighted versus exposure weighted LGD
• choosing the discount factor and the workout period
• economic downturn LGD
• modeling LGD using segmentation
• modeling LGD using regression
• shaping the Beta distribution for LGD
• modeling exposure at default (EAD): estimating credit conversion factors (CCF)
• cohort/fixed time horizon/momentum approach for CCF
• risk drivers for CCF
• CAP curves for LGD and CCF
• correlations between PD, LGD, and EAD
• calculating expected loss (EL)

• validating PD, LGD, and EAD models
• quality control
• quantitative versus qualitative validation
• use testing
• through-the-cycle (TTC) versus point-in-time (PIT) validation
• backtesting for PD, LGD, and EAD
• backtesting statistics, such as binomial, Vasicek, and chi-squared
• traffic light indicator approach
• backtesting action plans
• stress testing for PD, LGD, and EAD models
• static versus dynamic stress testing
• correlated trend analysis
• monitoring PD, LGD, and EAD models
• segmenting PD, LGD, and EAD models
• benchmarking
• internal versus external benchmarking
• Kendall's tau and Kruskal's gamma for benchmarking
• scorecard management
• low default portfolios (LDPs): implementation and validation
• value-at-risk (VaR) models
• the Merton/Vasicek model for calculating the regulatory capital

• review of traditional techniques for scorecard development
• neural networks: the neuron model, multilayer perceptrons (MLPs), training an MLP
• support vector machines: the SVM classification model and building scorecards using SVMs
• case study: using logistic regression and support vector machines to develop a country rating system

• survival analysis for developing customer lifetime models
• the censoring problem
• survival curves versus hazard curves
• Kaplan Meier analysis
• proportional hazards regression
• using survival analysis for LGD modeling and profit scoring
• risk-based pricing