IFRS 9 ECL Modeling Using Python: PD, LGD & EAD from Scratch

This course provides a complete, hands-on guide to developing IFRS 9 Expected Credit Loss (ECL) models in Python from scratch. It is designed for banking and finance professionals, credit risk analysts, auditors, data scientists, and students who want to understand and implement credit risk modeling based on the IFRS 9 accounting standard.

You will learn how to design, build, and validate all three components of the ECL framework: Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD). The course also explains important IFRS 9 concepts such as staging, Significant Increase in Credit Risk (SICR) assessment, lifetime versus 12-month ECL, discounting, and the use of macroeconomic scenarios for forward-looking adjustments.

Each section combines theory and practical Python implementation. PD modeling will focus on logistic regression and related validation metrics. LGD modeling will include linear, Beta, and Tobit regression methods, while EAD modeling will cover linear and logistic approaches for Credit Conversion Factor (CCF) estimation. You will use Python libraries such as pandas, numpy, statsmodels, and scikit-learn for data cleaning, variable selection, model development, and validation. You will also explore visualization and evaluation metrics to ensure that each model meets regulatory expectations.

By the end of the course, you will be able to design and implement an end-to-end IFRS 9 ECL model for retail or wholesale portfolios. You will understand how to connect PD, LGD, and EAD models to calculate total ECL and how to interpret results from both accounting and risk management perspectives.

No advanced programming experience is required. A basic understanding of finance, risk, or statistics will help you get the most out of the course.