Credit Risk Modeling for Basel and IFRS 9 using R and Python

📅 January 10th -11th
🌍 English

This comprehensive training to practical credit risk modeling provides a targeted training guide for risk professionals looking to efficiently build in-house probability of default (PD), loss given default (LGD) or exposure at default (EAD) models in a Basel or IFRS 9 context.  Combining theory with practice, this course walks you through the fundamentals of credit risk modeling and shows you how to implement these concepts using both R and Python software, with helpful code provided.  Throughout the course, the instructor(s) extensively report on their recent scientific findings and international consulting experience.  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, reference models, state of the art research insights and benchmarks.

Day 1

• Introduction to Credit Scoring
  • credit scoring for retail
    • application scoring
    • behavioral scoring
    • profit scoring
  • credit scoring for non-retail
    • prediction approach
    • expert based approach
    • agency ratings approach
    • shadow ratings approach
  • Big Data for credit scoring (social media data, call detail records, web scraping)
  • credit bureaus
  • credit ratings and rating agencies
  • privacy and ethics
• The Basel Accords and IFRS 9
  • regulatory versus economic capital
  • Basel Accords
  • PD versus LGD versus EAD
  • standard approach versus IRB approaches for credit risk
  • expected loss (EL) versus unexpected loss (UL)
  • Merton/Vasicek model
  • IFRS 9 (CECL)
  • code examples: calculating Basel regulatory capital in R/Python
• Data Selection, Sampling and Data Preprocessing
  • sample selection
  • variable types
  • missing values (imputation schemes)
  • outlier detection and treatment (box plots, z-scores, truncation, etc.)
  • exploratory data analysis
  • categorization (Chi-squared analysis, odds plots, etc.)
  • variable transformation: weight of evidence (WOE), Box-Cox, Yeo-Johnson
  • variable selection (information value, Cramer’s V)
  • reject inference
  • oversampling, undersampling, SMOTE
  • data quality and data governance
  • code examples: preprocessing credit risk data in R/Python

Day 2

• Developing PD Models
  • Level 1: Discrimination
    • logistic regression and decision trees
    • discrete time and continuous time hazard models
    • recent techniques: SVMs, random forests, XGBoost, deep learning (briefly)
    • measuring scorecard performance
    • splitting up the data: holdout sample, cross-validation, bootstrapping
    • ROC curve, CAP curve, and KS statistic
    • Expected Maximum Profit (EMP) measure
    • code examples: developing a PD scorecard in R/Python; calculating EMP in R/Python
  • o    Level 2: Ratings and Calibration
    • defining ratings: supervised versus unsupervised methods
    • monotonicity constraints
    • rating philosophy: Point-in-Time (PIT) versus Through-the-Cycle (TTC)
    • migration matrices
    • stability metrics
    • calibration methods
    • calibration uncertainty (sampling uncertainty, economic volatility)
    • Lexis diagrams
• Developing LGD Models
  • Level 0: Data
    • default definition
    • LGD definition
    • Basel versus IFRS 9 perspective
    • choosing the workout period
    • dealing with incomplete workouts
    • setting the discount factor
    • calculating indirect costs
    • drivers of LGD
  • o    Level 1: Discrimination
    • modeling LGD
    • segmentation (expert based versus regression trees)
    • (transformed) linear regression
    • fractional logistic regression
    • beta regression
    • two-stage models
    • measuring the performance of LGD models
    • code example: developing and evaluating an LGD model in R/Python
  • Level 2: Ratings and Calibration
    • defining LGD ratings
    • calibrating LGD
    • default weighted versus exposure weighted versus time weighted LGD
    • economic downturn LGD
• Developing EAD Models
  • Level 0: Data
    • defining exposure at default (EAD): conversion measures, credit conversion factors (CCF)
    • regulatory perspective
    • defining CCF (cohort, fixed time horizon, variable time horizon, momentum method)
    • risk drivers for CCF
  • Level 1: Discrimination
    • modeling CCF using segmentation and regression approaches
    • CAP curves for LGD and CCF
    • code example: developing and evaluating an EAD model in R/Python
  • Level 2: Ratings and Calibration
    • defining EAD ratings
    • calibrating EAD
  • correlations between PD, LGD, and EAD