It is often the case that it is not a single transaction that indicates an anomaly but a group of transactions such as a recurring monthly event. As such, normal outlier detection does not work as it is often a set of transactions that individually may seem to be normal or acceptable practices but collectively do not follow normal or acceptable practices. To address this, outlier detection is built in several layers to consider how rare a given business process may be and the outlier score of transactions based on other transactions in its group, and the ledger.

Anomalies such as financial misstatements, or fraud, are often not detected because they are carefully hidden among other similar transactions. Sophisticated fraudsters hide their work among similar account interactions and values.

This is where outlier systems need to consider many dimensions of the data at once. While human beings are good at seeing an outlier in 2 or 3 dimensions, a machine can do the same work tirelessly, considering 10 or more dimensions at a time. This capability helps machines to spot the outliers which are hiding next to normal activity and within an established process.