Basel IRB Credit Risk Models: A Practical Guide in R

Name: Basel IRB Credit Risk Models: A Practical Guide in R
Rating: 4.0 (20 reviews)

Develop and Validate Basel IRB Credit Risk Models Using R Programming

Created bySubhashish Ray

Last updated 3/2026

English

What you'll learn

Fundamentals of Credit Risk Modeling: Understand key concepts like Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD)
Building Basel-Compliant Credit Risk Models: Learn to develop PD, LGD, and EAD models aligned with Basel IRB standard
Practical R Programming for Credit Risk
End-to-End Modeling Workflow
Design, implement, and validate credit risk models step by step
Learn techniques for model calibration, backtesting, and monitoring
Regulatory and Business Insights: Understand how regulators assess Basel IRB compliance
Work on real-life datasets to understand practical challenges in risk modeling

Course content

5 sections • 37 lectures • 6h 14m total length

Introduction6:48
Explore Basel compliant advanced IRB credit risk modeling in R, building and validating PD, LGD, and EAD models with practical data and regression techniques.

Theoretical Understanding10:31
The Basel A-IRB framework defines probability of default as the one-year performance period default likelihood, with a 90 days past due threshold, through-the-cycle PD, and segments data into homogeneous pools.
Dataframe Variables7:45
Linear Regression Model (Benchmark Model)18:58
Logistic Regression Model12:34
Wald Test- Chi Square Test Statistics10:56
Creating Function to Compute Discriminatory Power12:37
Concordance, AUC & Model Evaluation13:10
Complementary Log Log (Cloglog) Regression Model14:10
Low Default Portfolios (LDP)11:49
Model Validation- Theory9:42
Model Validation- Practical17:53

Understanding LGD5:07
Understand loss given default (LGD), the portion of exposure a bank expects to lose after default, and how recoveries under Basel guidelines influence pricing, capital, and risk management.
Factors Affecting LGD6:29
Workout Period and Cooling Off Analysis6:20
Workout Method8:51
Apply the workout method to measure LGD by discounting post-default cash flows and incorporating costs, leading to recovery rate insights and Basel discounting approaches.
Chain Ladder Method12:33
LGDs outside the Interval [0,1]3:54
Segmentation for LGD Modeling9:31
Segment data by product, collateral, and risk drivers to capture distinct recovery and LGD patterns across mortgages, auto loans, credit cards, and SME loans.
LGD Modeling Base7:21
Dataset Description3:27
Summary Statistics10:03
Learn to load data into RStudio with read_xls, inspect the LGD data frame of 2542 observations and eight variables, and compute descriptive statistics using str, summary, and scheme.
Linear Regression Model15:32
Beta Regression Model16:45
Censored Regression (Tobit) Model17:33

Requirements

Basic Credit Risk Knowledge
R Programming Basics: Ability to write simple R scripts and use common libraries for data analysis
Foundational Statistics Skills: A basic understanding of probability, regression, and descriptive statistics
Eagerness to Learn: No prior experience in advanced modeling is needed—this course will guide you step by step
Basic Understanding of Financial Systems

Description

Are you looking to master credit risk modeling and understand the Basel IRB framework? Do you want to develop hands-on expertise using R programming for regulatory-compliant credit risk models? This course is designed for you!

"Basel IRB Credit Risk Models: A Practical Guide in R" is a comprehensive, step-by-step program that combines theoretical foundations with practical applications. Whether you’re a beginner exploring credit risk or an experienced professional looking to sharpen your modeling skills, this course will equip you with the knowledge and tools to excel.

What You’ll Learn:

Understand Credit Risk Metrics: Gain a solid foundation in Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD).
Master Basel IRB Compliance: Learn how Basel guidelines influence credit risk modeling in financial institutions.
Develop Models in R: Build, calibrate, and validate credit risk models using R, one of the most popular tools in data analysis.
Work on Real-Life Case Studies: Apply your skills to real-world scenarios and datasets for a practical understanding of risk analysis.
Perform Model Validation: Learn techniques for backtesting, stress testing, and model performance evaluation.
Regulatory and Business Insights: Understand how credit risk models shape banking decisions and regulatory compliance.

Why Take This Course?

Hands-On Learning: Practical implementation is at the heart of this course, guiding you step-by-step through the modeling process using R.
Real-World Relevance: Work on case studies and examples that replicate actual banking scenarios to gain practical insights.
Comprehensive Coverage: Learn everything from credit risk fundamentals to advanced topics like stress testing and portfolio-level analysis.
Beginner-Friendly Approach: Start with the basics—no prior experience with Basel IRB or advanced modeling is needed.

Who Should Enroll?

Aspiring credit risk analysts and data scientists.
Banking professionals aiming to deepen their knowledge of Basel IRB models.
Students and academics interested in financial risk modeling.
R programmers looking to specialize in credit risk analytics.

By the end of this course, you’ll not only understand how Basel AIRB credit risk models work but also gain the confidence to implement them effectively in R. Start your journey into credit risk modeling today!

Who this course is for:

Aspiring Credit Risk Analysts
Banking Professionals
Data Scientists and Analysts
Students and Academics
Finance and Risk Consultants
Programmers Interested in Finance
Anyone Interested in Basel IRB

Basel IRB Credit Risk Models: A Practical Guide in R

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 7min

Regulatory Environment6 lectures • 43min

Probability of Default (PD)11 lectures • 2hr 20min

Loss Given Default (LGD)13 lectures • 2hr 3min

Exposure at Default (EAD)6 lectures • 1hr 1min

Requirements

Description

Who this course is for: