Detect Fraud As It Happens

Identify false positives by using machine learning

The combination of legislation, market dynamics, and increasingly sophisticated fraud strategies requires you to be proactive in detecting fraud quicker and more effectively. Dynamic detection systems need to adapt to evolving compliance regulations, scale to deal with growing transaction volumes, detect sophisticated fraud patterns, and reduce false-positives. The combination of predictive analytics, streaming analytics, and business process management provides a powerful and cost-effective system for detecting fraud.

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Benefits

Faster Incident Resolution

With data discovery, statistical modeling, and aggregation within a single tool, you can create dashboards that pull from multiple databases and real-time information. Users can filter, slice and dice, and zoom in and out of data to determine if the alert needs further investigation or can be discarded as a false-positive.

Faster Surveillance Deployment

Surveillance projects can span multiple assets, with data and scenarios that range from monitoring abuse in cash equities to complex schemes involving foreign exchange (FX) derivatives. With connectivity to over 150 options, all your data feeds can be included using graphical flow development that reduces complexity and increases collaboration.

Faster Surveillance Deployment

Surveillance projects can span multiple assets, with data and scenarios that range from monitoring abuse in cash equities to complex schemes involving foreign exchange (FX) derivatives. With connectivity to over 150 options, all your data feeds can be included using graphical flow development that reduces complexity and increases collaboration.

Flexible Surveillance Scenarios

You’ll want to tailor thresholds that trigger alerts and exclude certain events from the analysis altogether. Perform correlation tests to detect complex potential abuses on one asset type so you can determine alerts and performance for related assets. This helps to maximize identification of anomalous data points and minimize false-positives.