IFRS9 Expected Credit Loss Model Development and Validation

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Subhashish Ray

4:49:16

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1 - Introduction

1 -Introduction.mp4

03:12

2 - Theoretical Framework

1 -IFRS 9 vs Basel III.mp4

05:30

2 -Loan Loss Distribution.mp4

05:54

3 -Objectives of IFRS9.mp4

02:22

4 -Improvement in Accounting Standards IAS 39 to IFRS9.mp4

03:01

5 -IFRS9 Staging Concept.mp4

06:44

6 -Significant Increase in Credit Risk (SICR) Assessment.mp4

04:51

7 -IFRS9 ECL Classification.mp4

03:17

8 -Expected Credit Loss (ECL) Components.mp4

08:46

3 - Probability of Default (PD)

1 -Default Definition.mp4

06:28

2 -Exploring the Dataset.mp4

09:20

2 -codes.pdf

2 -oneyearpd.xlsx

3 -Data Preparation.mp4

04:24

4 -Default Flag Creation.mp4

09:21

5 -Creating Training and Test Dataframes.mp4

07:27

6 -Information Value Assessment.mp4

09:22

7 -Binning and Weight of Evidence (WoE).mp4

11:29

8 -WoE Variables.mp4

03:52

9 -Correlation Check.mp4

04:44

10 -Pairs Correlation Analysis.mp4

03:13

11 -Fitting Logistic Regression Model (One Year PD).mp4

09:43

12 -Summary Statistics -Logistic Regression output.mp4

05:47

13 -Calibration.mp4

10:19

14 -Quantitative interpretation of Score Formula.mp4

03:07

15 -Model Validation Discriminatory Power.mp4

08:49

16 -One-Year PD vs Lifetime PD.mp4

07:56

17 -Censoring Mechanics- Multi Period Analysis (Lifetime PD).mp4

09:49

18 -Lifetime PD.xlsx

18 -Lifetime PD ML (Random Forest) Modeling.mp4

17:14

4 - Loss Given Default (LGD)

1 -LGD Theoretical Understanding.mp4

08:04

2 -LGD data elements.mp4

03:26

2 -data lgd year.xlsx

3 -LGD Database Creation.mp4

04:14

4 -Probability of Cure (PoC) Significance in LGD modeling.mp4

02:00

5 -Exploring the dataframe and performing exploratory data analysis.mp4

06:51

6 -Data Partitioning and Information Value Analysis.mp4

08:29

7 -LGD Regression Methods Tobit Regression.mp4

19:42

8 -LGD Regression Methods Beta Regression.mp4

12:21

5 - Exposure at Default (EAD)

1 -EAD Theoretical Understanding.mp4

09:47

2 -EADF Modeling.mp4

18:34

2 -ead.xlsx

3 -CCF Modeling.mp4

09:47

Description

Learn how to model and validate expected credit losses for IFRS9 using R Programming

What You'll Learn?

IFRS9 Expected Credit Loss (ECL) Staging
Understand the science and logic behind model development
Building predictive models with R Programming
Model Validation
Introduction to PD, LGD and EAD modeling
R Programming fundamentals for credit risk modeling

Who is this for?

Students

Risk Analytics Professionals

Statisticians

Experienced Risk Modelers

For someone who wish to start/shift their career towards risk modeling

What You Need to Know?

Basic Knowledge of R Programming

Computer with internet connection

Zeal and enthusiasm for learning a new skill

RStudio

Basic knowledge of credit risk components

Knowledge of basic statistical algorithms like logistic regression, beta and tobit regression

Knowledge of machine learning algorithms like random forest

More details

Description
This course will introduce you to the concept of provisioning, background of IFRS9 and the journey to forward looking impairment calculation framework. The staging allocation process is covered in depth before the introduction to the concepts of feeder models (PD, LGD and EAD models).Â
Despite the non-prescriptive nature of the accounting principle, common practice suggest relying on the so-called probability of default (PD), loss given default (LGD) and exposure at default (EAD) framework. Banks estimate ECL as the present value of the above three parameters product over a one-year or lifetime horizon, depending upon experiencing a significant increase in credit risk since origination. Three main buckets are considered : stage1 (one-year ECL), stage 2( lifetime ECL), stage3 (impaired credits).
The concepts of Next 12 month probability of default (PD), Lifetime Probability of Default, marginal default and modeling and validation concepts of LGD and EAD for IFRS9 has been explained step by step from scratch using R Programming.
This course also explains how Expected Credit Losses (ECL) affects regulatory capital and ratios. Modeling concepts for low default portfolios and scare data modeling is also covered. For modeling, both Generalized Linear Models (GLMS) and Machine Learning (ML) modeling methodologies has been explained step by step from scratch.

Who this course is for:
Students
Risk Analytics Professionals
Statisticians
Experienced Risk Modelers
For someone who wish to start/shift their career towards risk modeling

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R Programming

Subhashish Ray

Instructor's Courses

Hello, my name is Subhashish and I am passionate about teaching valuable skills to students who are motivated to learn and excel!Over the last 10 years, I have acquired valuable skills and experience that allow me to provide you with great learning experience and develop your skillset in various fields ranging from data science to banking domain (risk analytics).All of my courses are designed to cater wide variety of students ranging from beginners to advanced learners . My courses will help you gain real world skills and knowledge which you can apply directly into your work. If something remains unclear or you need help in understanding anything within my course, just send me a message and I will be happy to spend some one-on-one time with you in order to clarify and provide answers to your questions. I am available with you in your journey of learning from my courses at all time.Thank you for your interest in my courses and I look forward to seeing you in one of my lectures very soon.Best Regards,Subhashish

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