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Credit Risk Modeling

Course Details
Code: BB4C142
Tuition (USD): $3,400.00 • Classroom (4 days)

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 real-life case studies and exercises.

Skills Gained

  • 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 Can Benefit

  • Anyone who is involved in building credit risk models or is responsible for monitoring the behavior and performance of credit risk models

Prerequisites

  • 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.

Course Details

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
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