• views: 67
• date: 22 January 2026
• posted by: voska89

Learn PD, LGD, and EAD modeling with hands-on Python implementation under Basel IRB framework
What you'll learn
✓ How to understand and apply the Basel IRB framework for credit risk modeling
✓ Develop credit risk models under the Basel IRB framework using Python
✓ Understand the theory and regulatory requirements behind Basel IRB
✓ Prepare and analyze credit risk datasets for model development
✓ Build Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD) models
✓ Compute Risk-Weighted Assets (RWA) and capital requirements in line with Basel rules
✓ Apply statistical techniques such as logistic regression, beta regression, and CCF modeling
✓ How to validate credit risk models through discriminatory power, calibration, and backtesting
✓ How to translate regulatory requirements into practical, data-driven Python implementations
Requirements
● A basic understanding of credit risk or banking concepts is helpful but not mandatory
● Familiarity with Python programming at an introductory level
● General knowledge of statistics or econometrics will make learning easier
● No prior experience with Basel IRB models is required — everything will be explained step by step
● A general understanding of statistics and regression analysis will make the modeling sections easier to follow
● Familiarity with Python at an introductory level, including data handling with libraries like pandas, is expected, but advanced coding skills are not required
● Prior exposure to credit risk terms such as default, loss given default, and exposure at default is recommended but not mandatory
● Most importantly, bring a positive mindset and willingness to learn, because with practice and curiosity, you'll be able to master credit risk modeling step by step
Description
This course is your complete guide to mastering credit risk modeling under the Basel Internal Ratings-Based (IRB) framework using Python. It takes you from the fundamentals of Basel regulations to the practical implementation of Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD) models, giving you a clear and structured learning path. You will learn how to handle data, develop models step by step, and apply statistical techniques to ensure regulatory compliance. Along the way, you will explore logistic regression for PD modeling, regression and beta approaches for LGD, and Credit Conversion Factor (CCF) based methods for EAD. The course also demonstrates how these models feed into the computation of Risk-Weighted Assets (RWA) and capital requirements under the Basel IRB framework.
What makes this course unique is its balance between theory and hands-on practice. Each regulatory concept is explained clearly and then brought to life through Python coding, ensuring you can directly apply what you learn. By the end of the course, you will not only understand Basel IRB regulations but also be able to confidently develop, validate, and explain IRB-compliant models to regulators, auditors, and stakeholders. Whether you are a risk analyst, banking professional, data scientist, or student aspiring to build a career in credit risk, this course equips you with the essential skills and practical experience needed to succeed in the field.
Who this course is for
■ Credit Risk Professionals
■ Data Analysts and Statisticians
■ Finance and Risk Management Students
■ Python Users
■ Professionals transitioning into credit risk modeling
■ Aspiring Credit Risk Modelers
■ Banking and Finance Professionals
■ Data Analysts and Python Programmers
■ Risk Model Validators
Homepage

https://www.udemy.com/course/basel-irb-credit-risk-modeling-using-python/

   
   
   

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