Hybrid Neuro-Symbolic Fraud Detection: Guiding Neural Networks with Domain Rules
Hybrid Neuro-Symbolic Fraud Detection: Guiding Neural Networks with Domain Rules
I really thought I was onto something big: add a couple of simple domain rules to the loss function, and watch fraud detection just skyrocket on super-imbalanced data. The first run looked amazing… until I fixed a sneaky threshold bug and ran the whole thing across five different random seeds. Suddenly the “huge win” mostly evaporated.
What I ended up with instead was honestly way more useful: a reminder that on rare-event problems like fraud, the way we measure success (thresholds, seeds, metrics) can easily fool us more than the model itself. The rule does nudge the rankings a tiny bit better (you can see it consistently in ROC-AUC), but the real gains are small and fragile.
Here’s the full story—bugs, variance, lessons learned, and all.
The post Hybrid Neuro-Symbolic Fraud Detection: Guiding Neural Networks with Domain Rules appeared first on Towards Data Science.
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