Ifrs 9 Credit Risk Modelling: Pit Pd, Lifetime Pd & Ecl Sas

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.

Overview

Section 1: Introduction

Lecture 1 Introduction to IFRS 9 and PIT PD

Lecture 2 IFRS 9 PIT PD Modelling

Lecture 3 IFRS9 Three Pillar Overview

Lecture 4 Modelling Process

Lecture 5 Data Loading to SAS

Lecture 6 Data Quality Overview

Lecture 7 IFRS 9 PIT PD Model Development and ECL

Lecture 8 Splitting Data

Lecture 9 Feature Engineering

Lecture 10 Logistic Regression

Lecture 11 KS Statistic & AUC

Lecture 12 Model Calibration

Lecture 13 ECL Calculation

Lecture 14 Conclusion

Section 2: Data Preparation & Data Quality

Lecture 15 Introduction to data preparation and data checks

Lecture 16 Data Preparation

Lecture 17 Performance and Observation Windows

Lecture 18 Performance Window Example

Lecture 19 Vintage Analysis

Lecture 20 Wholesale Variables

Lecture 21 Retail Variables

Lecture 22 oversampling

Lecture 23 Data Quality

Lecture 24 Data Quality Procedures in SAS

Lecture 25 Mean Median Imputation

Lecture 26 Missing Data

Lecture 27 Handling Missing Values

Lecture 28 Missing Data Treatment

Lecture 29 Binary Indicator

Lecture 30 Working With Missing Values

Lecture 31 Cluster

Lecture 32 Rule Based

Lecture 33 Using Proc Logistic to handle Missing Data

Lecture 34 Regression

Lecture 35 Hot Deck

Lecture 36 Data Quality Summary

Lecture 37 Module Conclusion

Section 3: Variable Selection and Model Development

Lecture 38 Variable Selection and Model Development

Lecture 39 Exploratory Data analysis

Lecture 40 Exploratoy Data Analysis

Lecture 41 Multicollearity

Lecture 42 PROC VARCLUS

Lecture 43 Feature Engineering

Lecture 44 PROC VARCLUS Demo

Lecture 45 Variable Relavance

Lecture 46 Coarse Classing & Fine Classing

Lecture 47 Coarse and Fine Classing Demo

Lecture 48 Spearman & Hoeffdieng D Statistics

Lecture 49 Spearman & Hoeffding D Demo

Lecture 50 Variable Selection

Lecture 51 Stepwise Backward Forward Selection

Lecture 52 Variable Interactions

Lecture 53 Interactions

Lecture 54 Odds Ratio

Lecture 55 Model Development

Lecture 56 PROC LOGISTIC

Lecture 57 Logistic Regression

Lecture 58 Logistic Regression Demo

Lecture 59 Categorical Variables

Lecture 60 Grouping Categorical Variables Demo

Lecture 61 Greenacre method

Lecture 62 Greenacre Demo

Lecture 63 Testing logit linearity

Lecture 64 Lagged Variables

Lecture 65 Module Conclusion

Section 4: Model Validation & Perfomance Measurement

Lecture 66 Module Introduction

Lecture 67 Overview of Model Validation

Lecture 68 Hosmer & Lemeshow

Lecture 69 Barrier Score

Lecture 70 AUC & ROC

Lecture 71 Gini coefficient & Lift Chat

Lecture 72 AIC BIC & 2 LL

Lecture 73 Decile Ranking

Lecture 74 D Statistic Concordance

Lecture 75 PSI

Lecture 76 Model Evaluation & rerun

Lecture 77 Performance Summary

Lecture 78 Cross Validation

Section 5: PIT PD Calibration & Forward - Looking Adjustments

Lecture 79 PIT PD Calibration

Lecture 80 Forecasting and Scenario Analysis

Lecture 81 Stagging Overlay

Lecture 82 1PIT PD Calibration & Forward-Looking Adjustments

Lecture 83 Scenario Weighting

Lecture 84 Implementation in SAS PIT PD Calibration & Forward-Looking Adjustments

Lecture 85 PIT PD (12M) Calibration in SAS

Section 6: IFRS 9 Stagging & Significant Increase in Credit Risk

Lecture 86 IFRS 9 Staging

Lecture 87 IFRS 9 Staging and Basel Overlay

Lecture 88 Merton Model PD overlay

Lecture 89 Staging Evaluation & Validation Framework

Section 7: lifetime PIT PD Modelling

Lecture 90 Introduction

Lecture 91 survival model

Lecture 92 Cox Proportional Hazards

Lecture 93 Censoring

Lecture 94 Survival Functions

Lecture 95 Cox Regression in SAS

Lecture 96 Cohort Vintage Modelling

Lecture 97 Cohort Analysis

Lecture 98 Transition Matrices

Lecture 99 PROC IML Transition Matrices

Lecture 100 ECL Using Lifetime_PD

Lecture 101 Benefits of Lifetime PD

Lecture 102 PIT vs Lifetime_PD Stage Transitions

Section 8: ECL Calculation

Lecture 103 Introduction

Lecture 104 Regulatory ECL Reporting

Lecture 105 ECL Profit and Loss

Lecture 106 Classification Pillar

Lecture 107 12-Month Expected Credit Loss (ECL)

Lecture 108 Lifetime Expected Credit Loss (ECL) Part 1

Lecture 109 Advanced Portfolio Aggregation & Risk Insights

Lecture 110 Lifetime Expected Credit Loss (ECL) Part 2

Lecture 111 IFRS 9 Regulatory Reporting & Disclosures

Lecture 112 Sensitivity Analysis and Section Conclusion

Section 9: Automation and monitoring Framework

Lecture 113 Introduction

Lecture 114 Model Validation & Monitoring

Lecture 115 Model Deployment and Automation

Lecture 116 Automated IFRS-9 Workflows Architecture and Pipeline Design

Lecture 117 Automated IFRS-9 Workflows Controls Governance and Operations

Lecture 118 Model Performance and Data Quality

Lecture 119 Configure Alert Mechanisms

Lecture 120 End-to-End Automation and Monitoring Pipeline

Lecture 121 Module Wrap up and Overview

Section 10: Bonus Model : End to End IFRS PIT PD Model

Lecture 122 Introduction

Lecture 123 Demo : Bonus End-to-End IFRS9 Credit Risk PD Modelling

Lecture 124 Module Summary and Conclusion

Lecture 125 Closing and Congratulations

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.