False Alerts out of Control? Time for a New Approach.

One of the biggest challenges financial compliance and risk teams have to contend with is the high number of false positive alerts.

How big of a problem are false alerts? Based on my discussions with financial firms, compliance analysts at most tier one banks review about 1000 alerts a day. Even more frustrating, over 99% of these alerts turn out to be false.

Managing the deluge of alerts is a cumbersome task which can tap already stressed compliance resources and add to overall compliance costs.

One way to solve this problem is through the use of alert prediction.

Alert prediction is a form of predictive analytics which uses machine learning (a type of AI) to transform data into insights. To put it another way, historical alerts are analyzed using supervised machine learning. From this analysis of past alerts, the surveillance system learns how to accurately predict the outcome of new alerts.

Because alert prediction analyzes a myriad of data, the predictions can be enriched with information which ultimately helps compliance analysts save time and make better decisions. In my experience working with customers in the financial services sector, I»ve seen firms achieve significant benefits when alert prediction is integrated into their communications and market surveillance programs. This ranges from a significant reduction in false positives to increased efficiency and time-savings.

Interested in learning more?

I invite you to download the eBook I’ve authored titled "AI-Assisted Alert Reduction: Demystifying Alert Prediction in Financial Trade Surveillance". The eBook covers how alert prediction works, how enriched alerting data can empower better decision-making, the many benefits of alert prediction, and what you need to do to get started.

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