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June 28, 2024 - Greg Oprendek

5 Capabilities of Unsupervised Machine Learning You Might Not Know

Machine learning has been a key piece of fraud detection for decades. Along with rules engines, which offer simplicity and quick action, machine learning tools like neural networks and supervised learning help teams set fraud checks that prevent behavior from bad actors.

But when it comes to a modern fraud approach, no solution is truly ready to take on today’s fraud attacks without unsupervised machine learning. UML’s real-time adaptability is crucial, as it adapts to new threats without extensive training. But that’s not the only capability UML has, especially when it comes to benefitting fraud prevention teams. Here are five “secret” capabilities of UML in proactive fraud detection.

1. UML reduces false positives better than SML

UML does not require labeled data input and is designed to discover patterns within large amounts of unlabeled data. This enables the discovery of new and unknown threats. This often leads to the misconception that using UML results in false positives. That is not the case, as UML combines the power of anomaly detection, clustering analysis, and graph analysis to detect the relationship between anomalies or activities. It’s this deeper level relationship-based insight that creates fewer false positives and detects more fraudulent activities than SML.

  • The false-positive rate is much lower than with supervised machine learning because UML looks at data on a much largerscale instead of reviewing individual cases or relying solely on anomaly-based detection.
  • Because UML is based on large-scale observations, behaviors and patterns that are common with large organized crime rings that would otherwise go undetected under single case review can be identified quickly before fraud actually occurs.
  • ​​Since UML automatically identifies clusters of emerging threats in real-time, there is no human bias associated with model training.

2. UML is explainable and passes model governance

The goal of model governance is to ensure the quality and reliability of the model. To do this, users must understand how their fraud model work and how it arrives at certain conclusions. It’s commonly believed that UML exists in a “black box” that you can’t peek inside to see how it works, but that’s not the case.

  • UML provides specific reason codes why a certain transaction or activity was flagged as fraudulent.
  • These reasons are easily explained based on activities, behaviors, timing, and other factors.
  • Data in a UML model isn’t sensitive to outliers or data skews because data is dynamically observed in real time.
  • Each feature in the DataVisor platform uses a unique algorithm to locate patterns within specific data sets, with each algorithm featuring multiple layers of engineering that feeds into a clustering algorithm that creates the model.
  • DataVisor translates the data into visual representations to help users connect the dots.

3. UML is highly scalable

UML provides unmatched scalability for organizations because of DataVisor’s modern architecture. The detection engine is built on the latest big data infrastructure, which enables users to manage big data volume with high QPS and low latency to power real-time responses to emerging threats across hundreds of millions of accounts.

  • The DataVisor platform processes more than 4.2 billion user accounts with real-time-activity streams, an extremely high level of processing.
  • DataVisor’s system is fully distributed and able to handle millions of transactions in real time.

4. UML can be implemented in just 2 weeks

DataVisor’s platform has built-in flexibility that simplifies fraud modeling, including the option to build your own fraud models. Because UML doesn’t require extensive training or data labeling, organizations can usually get set up and start seeing results in as little as two weeks.

  • DataVisor’s complete fraud modeling platform simplifies UML and allows users to build a UML model themselves.
  • A manual mode allows clients to integrate data, engineer features and build or fine-tune models on their own.
  • DataVisor also offers templates that clients can follow for different scenarios.
  • Models can be tweaked to fit a company’s unique needs.
  • Users can get up and running in as little as two weeks.

5. UML reveals fraud clusters that anomaly detection can miss

Anomaly detection is one part of UML, but it’s not the end-all detection tool. Anomalies can happen for a number of reasons, and often result in a high false positive rate. However, when the relationships between anomalies are viewed, organizations can lower their false-positive rate because they have more insight as to whether an anomaly is truly suspicious.

  • Anomaly detection typically isn’t effective in fraud detection because it creates a lot of false positives.
  • Because of the high false-positive rate, anomaly detection often requires extensive manual intervention.
  • Fraud rings synchronize their behaviors instead of creating one-off fraud transactions, which may not appear as an anomaly when looking at individual user activities. This is why it’s important to go a few layers deeper and define the relationships between activities.
  • UML isn’t anomaly detection because it looks at clusters of activities rather than individual activities that don’t fit any specific patterns.

Discover the best UML in fraud prevention

DataVisor’s comprehensive fraud and AML solution suite combines patented unsupervised machine learning technology with native device intelligence and a powerful decision engine to provide protection for the entire customer lifecycle across industries and use cases. Schedule a personalized demo of our UML capabilities and see why we’ve been adopted by many Fortune 500 companies across the globe.

about Greg Oprendek
Greg is a passionate digital marketer, avid basketball fan, aspiring fraud expert, and Content Marketing Manager at DataVisor.
about Greg Oprendek
Greg is a passionate digital marketer, avid basketball fan, aspiring fraud expert, and Content Marketing Manager at DataVisor.