Why Imbalanced Data Breaks Standard Machine Learning

The Core Problem: In fraud detection, fraud might be 0.1% of transactions. A model that predicts "not fraud" for everything achieves 99.9% accuracy—but catches zero fraud. Standard ML algorithms optimize for overall accuracy, which incentivizes ignoring the rare class entirely.

Why Accuracy Fails

Accuracy treats all errors equally. In fraud detection, false negatives (missed fraud) cost thousands of dollars per case. False positives (blocked legitimate transactions) cost customer friction but are recoverable. The business cost is asymmetric, but accuracy does not know this. A model optimizing accuracy rationally ignores the minority class because the accuracy penalty for missing all fraud is only 0.1%.

The Gradient Signal Problem

During training, each batch contains mostly negative examples. Gradients from the majority class dominate parameter updates. The minority class contributes weak gradients that get averaged away. The model learns features useful for identifying negatives but never learns the subtle patterns distinguishing positives.

Key Insight: Imbalance is not just a data problem—it is a training dynamics problem. Even with perfect features, gradient-based optimization will underweight the minority class unless explicitly corrected.

Evaluation Metrics for Imbalanced Data

Replace accuracy with metrics that care about the minority class. Precision-Recall AUC measures performance across different operating points. F1 score balances precision and recall. Recall at fixed precision (e.g., recall at 90% precision) matches business constraints. Always evaluate on the natural class distribution, not artificially balanced test sets.

The Business Context

Define the cost ratio: how much worse is missing fraud versus blocking legitimate users? This ratio guides technique selection. If missing fraud costs 100x more than false positives, aggressive recall optimization is justified even at the expense of precision.