Failure Modes and Edge Cases in Production

Training Data Contamination

The most critical failure mode: anomalies present in training data. Unsupervised methods assume training data represents "normal," so contaminated data teaches models that fraud patterns are legitimate. Even 1-2% contamination significantly degrades detection. Mitigation: use robust preprocessing to remove statistical outliers before training, or employ semi-supervised approaches with some labeled normal examples.

Warning: Data contamination is insidious because models still appear to work—they just have reduced sensitivity to the exact fraud patterns present in training data.

Distribution Shift

Normal behavior patterns evolve: seasonal variations, new product launches, user behavior changes. Models trained on historical data may flag legitimate new patterns as anomalies (false positives) or miss evolved fraud tactics (false negatives). Implement continuous monitoring of anomaly score distributions and retrain when drift exceeds thresholds.

Adversarial Evasion

Sophisticated fraudsters craft transactions that mimic normal patterns to evade detection. Unsupervised methods are vulnerable because they define "normal" based on statistical properties that adversaries can learn to replicate. Defense: combine unsupervised detection with supervised models trained on known fraud patterns, and use ensemble diversity to make evasion harder.

Defense Strategy: Rotate model architectures and features periodically. Adversaries who learn current model boundaries face new detection surfaces after rotation.

Edge Cases

High-value legitimate transactions may appear anomalous due to rarity. Implement tiered thresholds: tighter thresholds for low-risk actions, looser for high-friction intervention. Cold-start users lack behavioral history, causing elevated false positives—use population-level baselines until sufficient individual data accumulates.