  Understanding the dynamics of financial transactions among people is
critically important for various applications such as fraud detection. One
important aspect of financial transaction networks is temporality. The order
and repetition of transactions can offer new insights when considered within
the graph structure. Temporal motifs, defined as a set of nodes that interact
with each other in a short time period, are a promising tool in this context.
In this work, we study three unique temporal financial networks: transactions
in Mercari, an online marketplace, payments in a synthetic network generated by
J.P. Morgan Chase, and payments and friendships among Venmo users. We consider
the fraud detection problem on the Mercari and J.P. Morgan Chase networks, for
which the ground truth is available. We show that temporal motifs offer
superior performance than a previous method that considers simple graph
features. For the Venmo network, we investigate the interplay between financial
and social relations on three tasks: friendship prediction, vendor
identification, and analysis of temporal cycles. For friendship prediction,
temporal motifs yield better results than general heuristics, such as Jaccard
and Adamic-Adar measures. We are also able to identify vendors with high
accuracy and observe interesting patterns in rare motifs, like temporal cycles.
We believe that the analysis, datasets, and lessons from this work will be
beneficial for future research on financial transaction networks.
