Unsupervised Machine Learning Engine
Read this Technical Deep Dive to Learn:
- The limitations in existing fraud detection and financial crime solutions
- Benefits of the unsupervised machine learning approach, compared to other detection methods
- How DataVisor Unsupervised Machine Learning Engine works in production
Current fraud detection and financial crime solutions failed to cope with the fast-changing attack landscape. Modern fraud attacks are organized and massive and adopt new techniques to mimic legitimate account behaviors. Existing solutions are failing to keep up as they are reactive in detection, examine events or accounts in isolation, and does not leverage all-new digital signals. Noticing the gaps, DataVisor created its flagship product, the Unsupervised Machine Learning engine.
We’ve developed a guide that talks about how DataVisor UML Engine uses an unsupervised machine learning approach to uncover hidden links among groups of bad actors.