Basel IRB Credit Risk Modeling using SAS: PD, LGD, EAD

Name: Basel IRB Credit Risk Modeling using SAS: PD, LGD, EAD
Rating: 3.4 (7 reviews)

A Hands-On Guide to build Basel regulatory credit risk models under Basel IRB using SAS

Created bySubhashish Ray

Last updated 12/2025

English

What you'll learn

Gain a comprehensive understanding of the Basel Advanced Internal Ratings-Based (A-IRB) approach and its role in regulatory capital requirements
Learn how to develop and implement Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD) using SAS
Master techniques for validating credit risk models in line with regulatory expectations and best practices
Use SAS to apply statistical methods, such as logistic regression and scorecard development, to enhance credit risk modeling
Gain practical experience in building and analyzing credit risk models using SAS
Develop the ability to prepare and manage large datasets for credit risk modeling in SAS

Course content

5 sections • 45 lectures • 7h 3m total length

Introduction6:05

Conceptual Understanding10:31
Dataset Variables7:45
PD Modeling Foundations: Dataset Overview, Target Definition & Portfolio Context12:30
PD Modeling: Univariate Analysis and Risk Driver Exploration25:10
PD Modeling Theory: Fine Classing, Coarse Classing, WOE and IV14:41
WOE & IV Implementation in SAS14:24
PD Model Development Using Logistic Regression (WOE-Based)14:23
Alternative PD Models: Probit & Complementary Log-Log (Challenger Models)22:08
Champion vs Challenger PD Models (Comparison Overview)10:34
Model Validation Fundamentals9:42
PD Validation: ROC, AUC & KS (Champion Model Testing)15:12
End of Section Quiz: PD Modeling

Understanding LGD5:07
Factors Affecting LGD6:29
Cooling off Analysis6:20
Workout Method8:51
Chain Ladder Method12:33
LGDs outside the Interval (0,1)3:54
Segmentation for LGD Modeling9:31
LGD Modeling Base7:21
Dataset Description3:27
LGD Data Exploration12:39
Correlation Analysis5:12
LGD Beta Regression in SAS: Data Preparation and Model Estimation10:21
Scoring and Diagnostics for LGD Beta Regression in SAS5:58
Beta vs Tobit Regression for LGD: Conceptual Comparison4:48
LGD Tobit Regression: Diagnostics and Residual Analysis12:14
Important Note: Only a small set of variables is used to keep the LGD model parsimonious, stable, and easier to validate, which is a key requirement under Basel IRB. We deliberately use only a few strong variables to avoid over-fitting and ensure model stability. Started with a small set of strong variables for stability and interpretability, and additional variables can be included later if they add predictive value and remain stable during validation. I encourage you to experiment by adding other variables to see how they impact model performance.
LGD Model Validation Results: MAE & RMSE Comparison (Beta vs Tobit)7:01
End of Section Quiz: Understanding LGD Modeling Concepts

Conceptual Understanding9:43
Credit Conversion Factor (CCF)7:11
CCF/EAD Computation11:23
Segmentation4:01
Dataset Variables6:14
EAD Modeling with CCF in SAS: Data Preparation, Beta Regression & Scoring12:51
Fitting the Beta Regression Model in SAS Using PROC NLMIXED7:17
In-Sample Scoring with the Beta Regression Model9:16
Tobit Regression for EAD Modeling in SAS Using PROC QLIM11:13
Diagnostics and Model Checks for the Tobit Regression Model9:13
Comparing Models Using Root Mean Square Error (RMSE) & Mean Absolute Error (MAE)7:17
End of Section Quiz: Exposure at Default (EAD) – Concepts and Modeling

Requirements

Interest in Basel Regulations
Basic knowledge of SAS or any other programming language will make it easier to follow the practical exercises
Basic Understanding of Credit Risk
Basic Knowledge of Banking or Finance
Access to SAS Studio or SAS OnDemand for Academics (a free, cloud-based version of SAS) is required to practice the exercises and run the provided scripts
A stable internet connection and a modern web browser are needed for accessing SAS OnDemand and course materials

Description

Welcome to "Basel IRB Credit Risk Modeling using SAS: PD, LGD,EAD"—a hands-on course designed to teach you how to build and implement Basel-compliant credit risk models using SAS. Whether you're a finance professional, data scientist, or student, this course will give you the tools and knowledge to effectively model credit risk metrics like Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD) within the Basel IRB framework.

What You’ll Learn:

Credit Risk Modeling Basics: Gain a strong understanding of key credit risk metrics, including PD, LGD, and EAD, and their role in the Basel IRB framework.
Basel IRB Compliance: Learn how Basel regulations shape the development and validation of credit risk models for regulatory compliance.
SAS for Credit Risk Modeling: Master the use of SAS programming to build and implement PD, LGD, and EAD models, including data manipulation and visualization.
Real-World Case Studies: Apply your learning to real-world banking scenarios and datasets, making the course relevant to actual financial industry practices.
Advanced Techniques: Learn advanced modeling concepts such as model calibration, backtesting, stress testing, and portfolio-level risk aggregation.

Why Take This Course?

Hands-On Learning: Engage with practical examples and build Basel IRB models using SAS step-by-step.
Industry-Relevant Skills: Acquire the knowledge needed to work on real credit risk projects in banking and finance.
Comprehensive Coverage: From foundational concepts to advanced techniques, this course covers all aspects of Basel IRB credit risk modeling.
Beginner-Friendly: Whether you're new to SAS or credit risk, this course starts with the basics and builds to more advanced topics.

Who Should Enroll?

Aspiring credit risk analysts or data scientists.
Banking professionals aiming to understand Basel IRB compliance.
SAS programmers looking to specialize in financial risk modeling.
Students and professionals interested in financial regulations and risk management.

By the end of this course, you'll not only understand Basel IRB credit risk models but also have the skills to implement them in SAS for practical, real-world use. Join today and enhance your expertise in credit risk modeling!

Who this course is for:

Risk Management Professionals
Data Analysts and SAS Users
Banking and Finance Professionals
Students and Career Changers
Academics and Researchers
IT and Data Science Professionals

Basel IRB Credit Risk Modeling using SAS: PD, LGD, EAD

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Course content

Introduction1 lecture • 6min

Basel Guidelines for IRB Modeling6 lectures • 43min

Probability of Default (PD)11 lectures • 2hr 37min

Loss Given Default (LGD)16 lectures • 2hr 2min

Exposure at Default (EAD)11 lectures • 1hr 36min

Requirements

Description

Who this course is for: