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.
