Develop practical capabilities in building credit scorecards from raw data.
Build a complete credit scorecard from raw core banking and alternative data. You apply Weight of Evidence, test performance with ROC, KS and Gini, handle reject inference and cut-offs, and document the scorecard for governance sign-off.
Practical modelling with Python and AI-assisted development
Use AI tools, including Claude Code, to accelerate model development, testing and documentation.
Understand the scorecard development framework
Profile and clean raw core banking and alternative data
Build an AI-assisted credit scorecard
Test whether the scorecard performs as expected
Handle incomplete application data and set cut-offs
Prepare the scorecard for governance sign-off
Use AI to speed up the build.
Use testing and reconciliation to prove the numbers.
Use scorecard expertise to make the judgement.
You will have worked through a practical end-to-end AI-assisted scorecard build, from raw core banking and alternative data through Weight of Evidence transformation, calibration, validation and governance-ready documentation.
| Duration | 2 days |
|---|---|
| Delivery | Instructor-led with live demonstrations and practical exercises |
| Investment | US$200 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.