XAI course
Master explainable AI for credit risk modelling: construct interpretable models, assess and address bias, set up monitoring and fairness reviews, and transform SHAP analysis into straightforward, stakeholder-approved explanations that fulfil regulatory standards and business requirements. This course equips you with practical tools to ensure your AI models are transparent, fair, and ready for real-world use in finance.

flexible workload from 4 to 360h
valid certificate in your country
What will I learn?
Gain hands-on skills to develop, interpret, and oversee credit risk models reliably. Explore data, engineer features, preprocess effectively, and train trustworthy, understandable models. Excel in global and local explanations, fairness checks, bias identification, and remedies. Produce straightforward reports, dashboards, and stories that meet stakeholder demands, aid choices, and comply with rules.
Elevify advantages
Develop skills
- Build transparent credit risk models: train, calibrate, and record predictions clearly.
- Use SHAP and global XAI methods: uncover vital factors and model patterns quickly.
- Develop fair, rule-compliant AI: spot bias, gauge effects, and apply fixes.
- Produce precise local explanations: apply SHAP, LIME, and what-if scenarios.
- Prepare XAI reports for stakeholders: build dashboards, APIs, and verifiable records.
Suggested summary
Before starting, you can change the chapters and workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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