Finding NeMo: Fishing in banking networks using network motifs

Xavier Fontes, David Aparício, Maria Inês Silva, Beatriz Malveiro, João Tiago Ascensão, Pedro Bizarro

Published at VLDB 2021 - Search, Exploration, and Analysis in Heterogeneous Datastores workshop

Machine Learning


Banking fraud causes billion-dollar losses for banks worldwide. In fraud detection, graphs help understand complex transaction patterns and discover new fraud schemes. This work explores graph patterns in a real-world transaction dataset by extracting and analyzing its network motifs. Since banking graphs are heterogeneous, we focus on heterogeneous network motifs. Additionally, we propose a novel network randomization process that generates valid banking graphs. From our exploratory analysis, we conclude that network motifs extract insightful and interpretable patterns.

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