A transformer-based model for credit card fraud detection

Credit card payments are one of the most common transaction methods in our daily life, such as online shopping, e-commerce, and mobile payment. However, with the extensive usage of credit cards, numerous credit card fraud transactions occur every year and cause a huge economic loss. In order to improve the detection performance, this project proposes a transformer-based model to conduct fraud detection. The proposed transformer-based model omits convolutional or recurrent operations and relies solely on attention mechanisms to extract dependencies in the sequence dataset. This project aims to develop a novel and efficient fraud detection approach for the parter financial organization.

Faculty Supervisor:

Weimin Huang

Student:

Partner:

NASDAQ Canada Inc

Discipline:

Engineering

Sector:

Finance and Insurance

University:

Memorial University of Newfoundland

Program: