IFRS 9 ECL Modeling Using Python: PD, LGD & EAD from Scratch
Published 4/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 7h 34m | Size: 4.22 GB
Learn IFRS 9 ECL modeling using Python with hands-on PD, LGD, and EAD model development and validation
What you'll learn
Understand the IFRS 9 Expected Credit Loss (ECL) framework and its key components - PD, LGD, and EAD
Develop Probability of Default (PD) models using Python with real-world simulated data
Build and validate Loss Given Default (LGD) and Exposure at Default (EAD) models step-by-step
Apply staging logic and compute 12-month and lifetime ECL in line with IFRS 9 requirements
Incorporate macroeconomic variables for forward-looking scenario adjustments
Automate ECL computation, aggregation, and reporting using Python libraries
Translate regulatory and accounting concepts into practical, data-driven model solutions
Gain confidence to design, document, and explain IFRS 9 ECL models to auditors and regulators
Understand the transition from IAS 39 to IFRS 9 and how ECL improves financial risk recognition
Perform data cleaning, preprocessing, and feature engineering for credit risk modeling using Python
Conduct model validation using performance metrics such as AUC, KS, Gini, Brier Score, and Concordance
Requirements
Basic understanding of banking, credit risk, or finance concepts (helpful but not mandatory)
Familiarity with Python programming fundamentals (variables, dataframes, basic libraries)
Some exposure to statistics or data analysis concepts such as regression, correlation, and probability
Enthusiasm to learn credit risk modeling and apply IFRS 9 concepts practically
A computer with Python installed (Jupyter Notebook, Google Colab, or any IDE)
No prior experience with IFRS 9 or advanced coding is required , all concepts are explained step-by-step
Description
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.
Who this course is for
Credit Risk Analysts, Modelers, and Risk Managers
Finance and Banking Professionals
Data Scientists, Machine Learning Practitioners, and Python Developers
Students and Researchers in Finance, Economics, or Quantitative Fields
Auditors, Compliance Officers, and Regulatory Professionals
Aspiring Risk Modelers or Python Learners
IFRS 9 ECL Modeling Using Python: PD, LGD & EAD from Scratch
