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Free Download IFRS 9 Credit Risk Modelling PIT PD, Lifetime PD & ECL SAS
Published 8/2025
Created by Taipa Gibon Huchu
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English + subtitle | Duration: 125 Lectures ( 8h 38m ) | Size: 3.27 GB
A practical guide to IFRS 9 credit risk modelling - covering PIT PD, Lifetime PD, ECL calculations, and validation COURS
What you'll learn
Build and calibrate Point-in-Time Probability of Default (PIT PD) models using SAS to align with observed default experience.
Apply forward-looking macroeconomic adjustments by integrating GDP, unemployment, interest rates, and other drivers into PD forecasts.
Implement multiple scenario approaches (base, upside, downside) with probability weighting to generate robust IFRS 9 Expected Credit Loss (ECL) estimates.
Develop automated SAS frameworks and macros for PIT PD modelling, calibration, scenario generation, and audit-ready reporting.
Design Lifetime PD models using transition matrices and survival analysis, and link them to ECL calculation engines.
Ensure regulatory compliance with IFRS 9 and Basel guidelines, including staging (SICR), documentation, and model validation best practices.
Requirements
Basic understanding of credit risk or finance concepts is helpful, but not mandatory.
Familiarity with statistics (probabilities, regressions, distributions) will make it easier to follow the modelling sections.
Some exposure to SAS is recommended, but all code will be explained step by step.
Learners should have access to SAS Studio (free trial or academic edition) or other SAS IDE
Most importantly: a willingness to learn, practice, and apply concepts in real-world credit risk modelling.
Description
Master IFRS 9 credit risk modelling with SAS. Learn PIT PD, Lifetime PD, staging, and ECL calculations step by step. Gain practical skills in data prep, feature engineering, calibration, model validation, and automation - build audit-ready, regulator-compliant models that enhance decision-making and career prospects.In today's rapidly changing financial landscape, organizations are under constant pressure to build resilient credit risk models that not only comply with regulations but also provide meaningful insights for decision-making. This course has been carefully designed to give you the skills, knowledge, and practical tools needed to develop, validate, and implement IFRS 9 Point-in-Time (PIT) and Lifetime Probability of Default (PD) models from start to finish.Across more than nine hours of step-by-step video content, you will learn how to transform raw credit data into regulatory-compliant, business-ready insights. We will start with the foundations of credit risk and IFRS 9 requirements before diving into data preparation, variable binning, Weight of Evidence (WOE) transformation, logistic regression modeling, and calibration techniques. You will also discover how to incorporate macroeconomic scenarios into your models, apply forward-looking adjustments, and overlay staging rules to align with IFRS 9 standards.Practical demonstrations are provided using SAS, ensuring you gain hands-on experience that can be directly applied in your professional role. By the end of the course, you will be able to confidently build and document models that satisfy auditors, regulators, and internal stakeholders.Whether you are a credit risk analyst, data scientist, actuary, or finance professional, this course will equip you with the tools to advance your career and help your organization navigate the challenges of modern risk management.
Who this course is for
Credit Risk Analysts and Risk Modellers who want to build, calibrate, and validate IFRS 9 Probability of Default (PD) and Expected Credit Loss (ECL) models.
Data Scientists and Statisticians seeking practical applications of SAS, Python, and statistical modelling techniques in financial risk management.
Finance and Banking Professionals working in credit risk, loan portfolios, or regulatory reporting who need to understand IFRS 9 requirements.
Actuarial Students and FRM Candidates preparing for professional exams who want real-world modelling examples and case studies.
Beginners in Credit Risk Modelling who may not have prior experience but are motivated to learn step by step, with code walkthroughs and hands-on examples.
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