An AI fraud detection system was trained on biased data and systematically approved fraudulent transactions from specific geographic regions.
A payment processor deployed an AI fraud detection system trained on historical transaction data. The training data had a significant blind spot: transactions from certain emerging markets had been manually reviewed and approved at higher rates, creating a pattern the AI learned as 'low risk.' Fraudsters discovered this vulnerability and routed transactions through these regions. The AI approved fraudulent transactions at rates far exceeding normal because the geographic origin was associated with low risk in the training data.
Training data bias where human review patterns created regional risk blind spots. No adversarial testing for geographic-based attack vectors. Insufficient monitoring for fraud pattern shifts post-deployment.
$890,000 in fraudulent transactions before pattern detected. Chargeback costs exceeded monthly projections by 400%. Merchant relationships strained due to fraud pass-through. Complete retraining of fraud model required.
Runplane could add a secondary governance layer over fraud decisions. Policies could flag unusual patterns such as sudden increases in approvals from specific regions or transaction patterns that deviate from historical baselines. These flagged transactions would be routed for additional review rather than automatic approval, even if the AI's fraud score is low.