Detecting Credit Transaction Fraudulent Behavior Using Recurrent Neural Networks

Fraudulent activities are hard to detect, but they cost financial institutions millions of dollars in monetary losses and legal costs every year. Millions of dollars are being lost in credit transactions as criminals are finding new, more sophisticated ways to conduct financial crime. This research project examines novel ways of detecting fraudulent behavior using powerful tools such as Recurrent Neural Networks, a type of machine learning model that is well suited for sequence or historical data.

Faculty Supervisor:

Lourdes Peña-Castillo

Student:

Ruben Antonio Chevez Guardado

Partner:

Verafin Inc.

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Memorial University of Newfoundland

Program:

Accelerate

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