Network based features for financial crime detection

US11640609B1 · kind B1 · utility

Assignee

• Wells Fargo Bank, N.A. · US

Inventors

• Wayne B. Shoumaker · San Francisco, US
• Harsh Singhal · Sunnyvale, US
• Suhas Sreehari · Berkeley, US
• Agus Sudjianto · Charlotte, US
• Ye Yu · Redmond, US

Key dates

• Filing date: Dec 13, 2019
• Grant date: May 2, 2023
• Expiry date: Apr 14, 2040

Classification

• Technology area (CPC G): Physics
• CPC primary: G06Q40/00
• WIPO field: IT methods for management
• WIPO sector: Electrical engineering

Abstract

Disclosed is an example approach in which network and non-network features are used to train a predictive machine learning model that is implemented to predict financial crime and fraud. Graphical network features may be generated by applying financial entity risk vectors to a network model with representations of various types of networks. The network model may comprise transactional, non-social, and/or social networks, with edges corresponding to linkages that may be weighted according to various characteristics (such as frequency, amount, type, recency, etc.). The graphical network features may be fed to the predictive model to generate a likelihood and/or prediction with respect to a financial crime. A perceptible alert is generated on one or more computing devices if a financial crime is predicted or deemed sufficiently likely. The alert may identify a subset of the set of financial entities involved in the financial crime and present graphical representations of networks and linkages.