Credit Scoring & Risk Modeling with Azure

Credit risk modeling demands more than predictive accuracy. Lenders, regulators, and auditors require systems that are transparent, reproducible, and demonstrably fair. This course equips data professionals with the end-to-end skills to build production-grade risk models on Azure Machine Learning, from workspace governance through live monitoring.

You will configure compliant Azure ML workspaces, build and evaluate classification models including Random Forest and XGBoost, and generate individual-level decision explanations using SHAP values. You will also automate the full model lifecycle through MLOps pipelines and monitor deployed models for drift, performance decay, and fairness issues across sensitive demographic groups.

Syllabus

Set up a secure, governance-ready Azure Machine Learning workspace that meets financial industry standards for data handling and compliance
Build and evaluate predictive models to classify customers into risk tiers (Low, Medium, High)
Generate clear, individual-level explanations for every credit decision using Azure interpretability tools, satisfying audit and regulatory requirements
Automate the entire risk model lifecycle using robust, repeatable MLOps pipelines in Azure
Monitor live production models for performance decay, data drift, and fairness issues across sensitive demographics, and trigger automated alerts for necessary updates