Develop practical capabilities in building and calibrating credit risk parameters.
Build PD, LGD and EAD models from a single raw account file. You calibrate and test each model, apply low-default-portfolio techniques, and document the work to the standard a credit committee expects.
Practical modelling with Python and AI-assisted development
Use AI tools, including Claude Code, to accelerate model development, testing and documentation.
Understand the PD, LGD and EAD framework
Prepare and understand the raw account file
Build an AI-assisted PD scorecard
Test whether the PD model performs as expected
Develop Loss Given Default estimates
Estimate Exposure at Default
Prepare models for credit committee review
Turn model outputs into trusted results
Use AI to speed up the build.
Use testing and reconciliation to prove the numbers.
Use credit risk expertise to make the judgement.
You will have worked through a practical end-to-end AI-assisted PD, LGD and EAD model build, from a single raw account file through calibration, validation and credit-committee-ready documentation.
| 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.