Advanced Learning System For Detection And Prevention Of Money Laundering

  • Published: Sep 21, 2017
  • Earliest Priority: Mar 18 2016
  • Family: 1
  • Cited Works: 0
  • Cited by: 0
  • Cites: 0
  • Additional Info: Full text
Abstract

An automated system for detecting risky entity behavior using an efficient frequent behavior-sorted list is disclosed. From these lists, fingerprints and distance measures can be constructed to enable comparison to known risky entities. The lists also facilitate efficient linking of entities to each other, such that risk information propagates through entity associations. These behavior sorted lists, in combination with other profiling techniques, which efficiently summarize information about the entity within a data store, can be used to create threat scores. These threat scores may be applied within the context of anti-money laundering (AML) and retail banking fraud detection systems. A particular instantiation of these scores elaborated here is the AML Threat Score, which is trained to identify behavior for a banking customer that is suspicious and indicates high likelihood of money laundering activity.


Claims
Download PDF
Document Preview
Document History
  • Publication: Sep 21, 2017
  • Application: Mar 18, 2016
    US US 201615074977 A
  • Priority: Mar 18, 2016
    US US 201615074977 A

Download Citation


Sign in to the Lens

Feedback