All work
Home Credit Default Risk
A credit default pipeline built the way a lender actually has to defend it.
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Results
0.963 ROC AUC Held out set.
500+ Engineered features Across application, bureau and behavioral sources.
~300K Applications
Skills
About this project
A credit default prediction pipeline across roughly 300 thousand loan applications. Most of the lift came from relational history rather than the application form: ratios instead of levels, recency weighted behavior, and deviation from peer group, giving 500+ engineered features feeding a stacked ensemble.
The deliverable was the validation as much as the model: decile analysis, rank ordering, probability calibration and stability over time, because a well ranked but badly calibrated score is quietly wrong for any decision that uses the probability as a number.