Alternative Data in ML Scoring: Methods and Limitations
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
Bureau data does not exist for a large share of applicants in Canada and elsewhere. Alternative data sources have generated significant interest as a way to extend credit access — but the technical and regulatory complications are real and often understated.
What counts as alternative data
The course covers four source categories: open banking cash flow data, telco payment records, rental and utility histories, and device or app behavior signals. Each has distinct acquisition methods, quality issues, and legal constraints.
No single source works universally well. Rental history predicts well for some demographics and poorly for others. Device data raises privacy questions that vary by province. These trade-offs are examined directly.
Feature extraction challenges
Raw open banking data arrives as unstructured transaction strings. Categorizing income and expense flows requires text normalization, merchant tagging, and careful handling of irregular income patterns like freelance or gig work.
Alternative data modeling is slower than it looks in conference presentations. Data cleaning alone typically consumes more time than model training.
Regulatory and fairness considerations
PIPEDA compliance, consent requirements for open banking data, and disparate impact risk from proxy variables are addressed in the final module. Deploying alternative data models without this layer creates legal exposure that erases any lift in predictive power.
program curriculum
structured by topic period and learning outcome
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Alternative Data Landscape
Week 1Source taxonomy, data acquisition methods, quality benchmarks, jurisdiction-specific legal constraints.
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Open Banking Feature Engineering
Week 2Transaction categorization, income stability metrics, cash flow volatility features, handling gig economy income patterns.
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Telco and Utility Data Integration
Week 3Payment regularity features, data linkage methods, missing data rates by demographic segment.
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Model Integration and Lift Analysis
Week 4Combining bureau and alternative features, measuring incremental Gini, avoiding multicollinearity with bureau variables.
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Compliance and Deployment
Week 5PIPEDA consent frameworks, disparate impact testing on alternative features, documentation for model risk management.
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.
9 seats remaining