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

Jon Ander Gómez, Juan Arévalo, Roberto Paredes, J. Nin*

*Autor correspondiente de este trabajo

Producción científica: Artículo en revista indizadaArtículorevisión exhaustiva

56 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Páginas (desde-hasta)175-181
Número de páginas7
PublicaciónPattern Recognition Letters
Volumen105
DOI
EstadoPublicada - 1 abr 2018
Publicado de forma externa

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