End-to-end neural network architecture for fraud scoring in card payments

Abstract

[EN] Millions of euros are lost every year due to fraudulent card transactions. The design and implementation of efficient fraud detection methods is mandatory to minimize such losses. In this paper, we present a neural network based system for fraud detection in banking systems. We use a real world dataset, and describe an end-to-end solution from the practitioner's perspective, by focusing on the following crucial aspects: unbalancedness, data processing and cost metric evaluation. Our analysis shows that the proposed solution achieves comparable performance values with state-of-the-art proprietary and costly solutions. (c) 2017 Elsevier B.V. All rights reserved.Gomez, J.; Arévalo, J.; Paredes Palacios, R.; Nin, J. (2018). End-to-end neural network architecture for fraud scoring in card payments. Pattern Recognition Letters. 105:175-181. https://doi.org/10.1016/j.patrec.2017.08.02417518110