ML Scoring Models: From Raw Data to Credit Decisions
Machine learning methods applied to credit scoring have shifted from theoretical research to standard practice. The models in this program reflect that shift — built from data patterns, validated on real portfolios, and interpreted with tools that regulators and risk teams actually use.
about this program
Credit scoring with machine learning sits at the intersection of statistics, software engineering, and financial regulation. Getting one part wrong can mean rejecting creditworthy applicants or underpricing risk at scale.
What this course covers
You will work through the complete model development cycle: sourcing and cleaning bureau data, handling class imbalance, selecting and tuning classifiers, and generating outputs that satisfy internal audit requirements.
The course uses real-world tabular datasets with the kinds of gaps and inconsistencies you encounter in production — not cleaned Kaggle samples.
Technical stack used
All exercises run in Python using scikit-learn, LightGBM, and Optuna for hyperparameter search. You will also use SHAP for explainability, which is increasingly required under fair lending frameworks in Canada and the EU.
Scoring is not just about AUC. A model that cannot be explained to a compliance officer is a model that cannot be deployed.
Who this is designed for
Analysts and data scientists who already work with structured data and want to move into credit risk or improve existing scoring pipelines. Some prior experience with pandas and basic classification models is assumed.
program curriculum
structured by topic period and learning outcome
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Data Foundations
Week 1Credit bureau file formats, application data schemas, handling missing values in financial features, time-based train/test splitting.
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Feature Engineering for Risk
Week 2Weight of Evidence encoding, monotonic binning, delinquency trajectory features, vintage analysis.
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Model Selection and Calibration
Week 3Logistic regression baselines, gradient boosting, probability calibration, Gini and KS metrics.
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Explainability and Adverse Action
Week 4SHAP values in scoring context, generating reason codes, documenting model decisions for regulatory review.
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Deployment and Monitoring
Week 5Score card export, PSI-based drift detection, setting up automated retraining triggers.
ready to work with REAL scoring data?
The program runs on actual credit portfolio datasets under controlled conditions. You leave with code, documentation, and a validated model — not slides. Enrolment closes when seats fill.
14 seats remaining