Develop practical capabilities in expected credit loss modelling under IFRS 9.
Build a working IFRS 9 expected credit loss engine from loan-level data. You work through staging, PD, LGD and EAD, scenario weighting and lifetime ECL. You finish with results that reconcile, sensitivities you understand and documentation ready for review.
Practical modelling with Python and AI-assisted development
Use AI tools, including Claude Code, to accelerate model development, testing and documentation.
Understand the core ECL framework
Prepare and understand loan-level data
Build an AI-assisted Probability of Default model
Test whether the PD model performs as expected
Develop Loss Given Default estimates
Estimate Exposure at Default
Bring the ECL components together
Turn model outputs into trusted results
Use AI to speed up the build.
Use testing and reconciliation to prove the numbers.
Use IFRS 9 expertise to make the judgement.
You will have worked through a practical end-to-end AI-assisted IFRS 9 ECL modelling workflow, from data preparation through PD, LGD and EAD modelling, staging, ECL calculation, testing, reconciliation and reporting.
| Duration | 3 days |
|---|---|
| Delivery | Instructor-led with live demonstrations and practical exercises |
| Investment | US$300 per delegate |
| Group bookings | 20% discount |
| Prerequisites | Laptop, working knowledge of credit risk and Excel |
| Tools | Python and AI-assisted development, including Claude Code |
| Facilitator | Tafirei Mashamba, PhD |
| Next intake | Ask us for the next intake. We reply by email or WhatsApp. |
Need this built for your institution instead? See Credit Risk Modelling.
Book your seat, or ask about training your whole team. Group bookings receive a 20% discount.