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An over-reliance on static rules
Many transaction monitoring systems depend on simple rules to pinpoint anomalies: How many transactions have occurred over the last week? Is this behavior in line with the client’s past behavior? These kinds of simple rules will turn up known anomalies, but they don’t take advantage of context and inevitably miss part of the picture.
Too many false negatives
Smart criminals are often aware of the kind of controls financial institutions have in place to catch them in the act. They have long adapted to those controls and know how to quietly bypass them - or to evade detection long enough to succeed.
Too many false positives
Because they fail to take context into account, those static rules also turn up a lot of results that are in fact benign. AML teams then spend too much of their limited time and resources chasing false positives instead of investigating actual criminal activity and adapting to new threats.
The pitfalls of a traditional transaction monitoring approach
Traditional transaction monitoring approaches come with certain drawbacks that can leave the organizations that depend on them vulnerable to high risk activity.
Navigating millions of records for effective context-based monitoring
Transaction Monitoring
More effective investigations
Graph visualization makes it easy to understand the context around any situation. Act quickly and confidently instead of wasting time chasing information.
Graph analytics can be used for more effective transaction monitoring
Graphs offer several advantages when layered into transaction monitoring systems:
Reduce false negatives
By analyzing entire networks, graph analytics are effective in uncovering complex criminal schemes as they emerge.
Add context to ML models
Leverage graph analytics and graph embeddings to provide extra input to your machine learning models.
Because you can't catch what you can't see
Fraudsters or money launderers know how to slip through the cracks of traditional systems, often exploiting gaps across departments, customer journeys, or products to avoid detection.
To stop them, you have to see them. And that starts with piecing together the bigger picture of your data.
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