Fraud Detection
Using AI techniques (e.g., anomaly detection, pattern recognition) to identify and prevent fraudulent activities in finance, insurance, etc.
Definition
Involves supervised (classification) and unsupervised (anomaly detection) models that analyze transaction patterns, network graphs, and user behaviors. Governance must balance detection sensitivity with false-alarm rates, conform to financial-crime regulations, and integrate explainability so investigators understand why alerts were raised. Continuous model updating is essential to counter evolving fraud tactics.
Real-World Example
A payment-processor uses a hybrid system: a supervised model flags known fraud patterns (stolen cards), and an unsupervised autoencoder detects anomalies (unusual transaction amounts). Alerts above a risk threshold trigger real-time transaction holds, reducing fraud losses by 60% while keeping false positives below 5%.
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