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Machine learning for anomaly detection Meaning

The use of Artificial Intelligence to identify patterns in data that do not "fit" the normal behavior. In finance, this is the primary tool for catching fraud, money laundering, and hacking attempts in real-time across millions of transactions.

The machine learning model is "trained" on years of normal transaction data. It learns what a "normal" day looks like.

If it suddenly sees a "burst" of small transactions to a high-risk country, or a "smart contract" being called in a way that has never happened before, it flags it as an "anomaly" for immediate human review. This technology is "adaptive." As scammers develop new techniques, the AI learns from the new data and adjusts its "detection rules" automatically.

This is far more effective than "static" rules (like "flag every trade over $10,000"), as it can catch sophisticated, "low-and-slow" attacks that would otherwise fly under the radar.

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