Anomaly detection is an advanced technique that uses an organisations’ data and machine learning to detect behaviour that doesn’t fit within the expected normal behaviour profile. This module works an additional filter for suspicious transactions over and above the ComplyRadar rules engine.
There are different types of anomalies that can be detected, including:
- Point anomalies; which are single instances (or outliers) that fall outside of the normal behaviour. These include sudden large deposits or credits to an account.
- Contextual anomalies; which are data points or transactions that appear anomalous without the appropriate context. For example, increased spending during the month of December would appear anomalous in itself but in the context of the holiday season, would be considered normal.
A collective anomaly is when a number of data points are considered anomalous, however the values of the individual data points are not themselves anomalous. An example of a collective anomaly includes a case where small deposits below thresholds are affected, however the total value raises a suspicious transaction.
Determining whether a transaction is likely to be fraudulent in many cases is not a binary response of suspicious/not suspicious, but rather the likelihood of being fraudulent. The ComplyRadar ML module picks those transactions, over and above the rules, that have the highest likelihood of being suspicious.
In addition to providing a likelihood, the ComplyRadar Module allows financial institutions to reduce the high levels of false positives generated by rules-based systems. Less false positives allow compliance and fraud prevention staff to focus on investigating true compliance risks and fraudulent cases.
Finally, the ComplyRadar ML module will allow financial institutions to identify suspicious transactions that are not within the space being scanned by the rule engine. For example, the rule engine could be configured to scan particular conditions, and anything not falling within those checks would not be captured. The ComplyRadar ML module will capture any suspicious transactions by all possible combinations of data fields, client profiles and segments.
Contact us to learn how ComplyRadar utilises AI Anomaly Detection and machine learning, providing you with the power of true analytics to inspect and act on suspicious transactions in real-time.