Master IFRS 9 Credit Risk Modelling by Nitin Kumar on Maven

Master IFRS 9 Credit Risk Modelling

Nitin Kumar

Course is in session

July 25—Aug 9, 2026

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Build production-grade ECL models from scratch - PD, LGD, EAD, scenario analysis

IFRS 9 fundamentally changed how banks and financial institutions recognise credit losses. Where IAS 39 let firms wait until a loss had already occurred, IFRS 9 demands a forward-looking Expected Credit Loss (ECL) model - and getting it wrong has direct P&L consequences, regulatory scrutiny, and audit risk.

Yet most practitioners learn IFRS 9 piecemeal: a seminar here, a whitepaper there, no coherent framework that takes you from raw data to a fully validated ECL engine. This course closes that gap.

In 24 hours of live, instructor-led sessions you will move through the entire IFRS 9 modelling lifecycle - from the three-stage impairment framework to PD, LGD, and EAD modelling, macroeconomic scenario integration, and regulatory-grade validation. Every concept is implemented in real code (R and SAS) on realistic datasets, so you leave with skills you can apply on Monday morning.

Whether you are building models for the first time, validating someone else's work, or preparing for a regulatory review, this course gives you the technical depth and practical confidence to do it well.

What you’ll learn

Explain the IFRS 9 framework end-to-end - the three-stage impairment model, SICR assessment, and how it differs from both IAS 39
Build Probability of Default (PD) models
- Build Probability of Default (PD) models using logistic regression, WoE/IV transformations, fine and coarse classing
Develop Loss Given Default (LGD) models
- Develop Loss Given Default (LGD) models for secured and unsecured exposures, including downturn LGD, beta regression, and two-stage cure
Model Exposure at Default (EAD)
- Model Exposure at Default (EAD) across term loans, revolving facilities, and off-balance sheet items using credit conversion factor (CCF)
Incorporate forward-looking macroeconomic scenarios
- Incorporate forward-looking macroeconomic scenarios (base, upside, downside) into PIT-PD satellite models and calculate ECL

Learn directly from Nitin

Nitin Kumar

Credit risk professional with deep expertise in IFRS 9 and credit risk modelling.

Nitin Kumar is a credit risk professional with deep expertise in IFRS 9 and regulatory credit modelling across retail and wholesale banking portfolios. He has hands-on experience building and validating PD, LGD, and EAD models for major financial institutions, and has worked closely with risk teams navigating IFRS 9 implementation, model governance, and audit review.

Nitin brings a practitioner's perspective to every session - focusing on the real-world challenges that textbooks skip: messy data, low-default portfolios, regulatory push back, and the gap between theory and production code. His teaching style combines rigorous technical content with worked examples in R and SAS, ensuring participants leave with both understanding and usable skills.

Who this course is for

Credit risk analyst or modeller looking to move beyond IAS 39 and build rigorous, compliant ECL models
Model validation specialist who needs to assess PD, LGD, and EAD models against regulatory and best-practice standards
Financial controller or accountant wanting to understand the quantitative mechanics driving IFRS 9 numbers

What's included

Live sessions
- Learn directly from Nitin Kumar in a real-time, interactive format.
Certificate of completion
- Share your new skills with your employer or on LinkedIn.
Maven Guarantee
- Your purchase is backed by the Maven Guarantee.

Course syllabus

Week 1

Jul 25—Jul 26

Session 1 — IFRS 9 Fundamentals Part 1

1 item

Session 2 — IFRS 9 Fundamentals Part 2

1 item

Week 2

Jul 27—Aug 2

Session 3 — Probability of Default (PD) Modelling

1 item

Session 4 — LGD and EAD Modelling

1 item

Schedule

Live sessions

24 hrs

Build production-grade ECL models from scratch — PD, LGD, EAD, scenario analysis, and validation — in 6 intensive live sessions with hands-on R and SAS practice.

Projects

36 hrs

Build Probability of Default (PD) models using logistic regression, WoE/IV transformations, fine and coarse classing, and survival analysis — in both R and SAS
Develop Loss Given Default (LGD) models for secured and unsecured exposures, including downturn LGD, beta regression, and two-stage cure/recovery models

Async content

12 hrs

Incorporate forward-looking macroeconomic scenarios (base, upside, downside) into PIT-PD satellite models and calculate probability-weighted ECL.

Testimonials

I'd been producing IFRS 9 numbers for two years without truly understanding where they came from. This course gave me the language, the logic, and the code to own the whole model lifecycle.
I could tick boxes on a validation template, but I couldn't look a model developer in the eye and say 'your downturn LGD assumption is wrong, and here's why.' Now I can — and I do.
My auditors were asking questions I was passing straight to the quant team. After this course, I could answer them myself — and challenge the model outputs when they didn't make commercial sense.

Frequently Asked Questions

What happens if I can't make a live session? You can access recordings whenever you need to.
Do I need programming experience to take this course? Yes — a basic working knowledge of R or SAS (or a comparable statistical language such as Python) is expected. You do not need to be an expert, but you should be comfortable writing and running scripts. Full code will be provided for every model and exercise.
What software do I need? R (version 4.0+) with RStudio, and SAS (version 9.4+) or SAS University Edition. Microsoft Excel is also useful for data review. A full setup guide and R package installation script will be sent before Session 1.
I work in Python, not R or SAS. Will this still be useful? Yes. The statistical methods, model design principles, and validation frameworks are language-agnostic. Code is provided in R and SAS, but the logic translates directly to Python (scikit-learn, statsmodels, lifelines etc.). Many participants adapt the provided code to their preferred environment.
Is this course relevant outside the UK? Absolutely. IFRS 9 is mandatory across the EU, UK, Asia-Pacific, the Middle East, and most other major jurisdictions. US participants working under CECL will also find significant overlap. The course is taught in UK timezone; recordings are available for all sessions.
What level of credit risk experience do I need? This is an advanced professional course. You should already understand core credit concepts (default, collateral, credit scoring, loan products) and have familiarity with regression analysis. It is not suitable for complete beginners to credit risk or statistics.
What's the refund policy? Your purchase is backed by the Maven Guarantee.