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. Broadly speaking, a true anomaly is one that deviates from the norm of the expected or the familiar. Anomalies can be the result of incompetence, maliciousness, system errors, accidents or the product of shifts in the underlying structure of day-to-day processes.
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.
There are different ways to address the challenge of anomaly detection, including supervised and unsupervised learning.
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.
The challenge of detecting anomalies
Anomaly detection presents a unique challenge for a variety of reasons. First and foremost, the financial services industry has seen an increase in the volume and complexity of data in recent years. In addition, a large emphasis has been placed on the quality of data, turning it into a way to measure the health of an institution.
To make matters more complicated, anomaly detection requires the prediction of something that has not been seen before or prepared for. The increase in data and the fact that it is constantly changing exacerbates the challenge further.
Leveraging machine learning
There are different ways to address the challenge of anomaly detection, including supervised and unsupervised learning.
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