City of Rochester
The short version
The full story
The City of Rochester had a fraud model at 62% accuracy that nobody trusted. When it flagged a case, the loan officers couldn’t tell why, so they second-guessed every decision.
What I built
An explainable fraud-detection model using XGBoost, with SHAP for explainability. Accuracy went from 62% to 73%, but the bigger win was that every flagged case came with a one-line explanation of which features drove the score.
Why it mattered
The officers stopped second-guessing, because they could finally see the evidence. That’s the point I keep coming back to: in production ML, the accuracy lift only matters if the people using it can act on it.
The model supports the humans making the call. It doesn’t replace their judgment, it shows its work so they can trust it.
Stack
Python, XGBoost, SHAP. Delivered through Simon Vision Consulting for the City of Rochester.