Featured Project

Loan Performance Analytics & Risk Intelligence
End to end analytics platform built on 992K Freddie Mac loans ($248B portfolio) spanning 11 origination years. SQL powered analytical engine with automated data quality checks, interactive Tableau dashboards, ML based risk scoring, and auto generated executive reports.
ML Risk Model: Results Explorer
Dual model approach: OriginRisk (origination predictor, honest AUC) + SegmentIQ (behavioral segmentation, risk ranking)
For complete methodology and architecture details, see the Technical Documentation.
| Credit Band | LTV | Rate | Vintage | Loans | Risk Score | Actual DLQ |
|---|
The dataset is a single monthly snapshot, not a time series. True forward prediction requires sequential monthly data. The origination model predicts current delinquency status from borrower profile, which is valuable for risk stratification but not a deployment ready forecasting tool.
Only about 1% of loans are delinquent. Precision is inherently low. Addressed with balanced class weights and evaluated using lift metrics rather than raw precision.
Monthly time series data, additional features (employment, payment amounts, forbearance history), gradient boosting ensembles, and a calibration layer for dollar loss estimation.
The first attempt produced AUC = 1.0 due to data leakage. The redesign separates OriginRisk (origination, AUC ~0.77) for honest prediction from SegmentIQ (behavioral) for segmentation. This distinction is what mortgage analytics teams value.
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