Behavioral Scoring Models: Predicting Risk Over Time
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
Application scoring uses data available at the moment of a credit request. Behavioral scoring uses what happens afterward — payment timing, balance trajectories, product usage — to continuously reassess risk across an existing portfolio.
Different data, different methods
Transaction sequences and balance histories require feature engineering approaches that do not appear in standard ML courses. Rolling windows, lag features, recency-frequency-monetary variables, and trend indicators all play a role.
The course covers how to construct a behavioral feature matrix from raw transaction logs, including how to handle accounts with short histories or irregular activity.
Practical portfolio applications
Behavioral scores feed into collections prioritization, credit limit management, and early warning systems. You will build each of these three use cases from the same underlying model with different decision thresholds and output formats.
The same model can be used for very different decisions depending on how the output is consumed. The course makes this explicit with separate notebooks for each use case.
Time-series validation
Standard cross-validation breaks down with time-ordered data. Walk-forward validation and expanding window strategies are covered in detail, with worked examples showing how naive splitting inflates performance metrics.
program curriculum
structured by topic period and learning outcome
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Behavioral Data Architecture
Week 1Transaction log schemas, account-level aggregation, handling variable observation windows.
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Time-Series Feature Engineering
Week 2Rolling statistics, lag features, RFM variables, delinquency trajectory encoding.
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Model Training and Selection
Week 3Gradient boosting on behavioral features, monotonicity constraints, temporal train/test splits.
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Three Portfolio Use Cases
Week 4Collections prioritization, credit limit review triggers, early warning system thresholds.
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Walk-Forward Validation
Week 5Expanding window methodology, comparing Gini across time periods, detecting model decay.
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.
11 seats remaining