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Financial firms should leverage machine learning to make anomaly detection easier

Anomaly detection is one of the more difficult and underserved operational areas in the asset-servicing sector of financial institutions.

For the financial services industry, detecting anomalies is critical, as they may be indicative of illegal activities such as fraud, identity theft, network intrusion, account takeover or money laundering, which may result in undesired outcomes for both the institution and the individual.

Detecting outlier data, or anomalies according to historic data patterns and trends can enrich a financial institution’s operational team by increasing their understanding and preparedness.