One of the chief uses of deep learning in enterprise is fraud and anomaly detection.
Anomaly detection is a broad term referring to any set of unusual activities, including network security breaches, extraordinary transactions or even mechanical breakdowns. Any behavior that be digitized or measured numerically, including machine performance, is subject to anomaly detection.
Fraud detection is a good example of anomaly detection for many reasons, the first being that it is incredibly costly. Fraudulent transactions are estimated to cost U.S. banks up to $11 billion per year, so it’s a problem that a lot of people want to solve.