Temporal Motifs for Financial Networks: A Study on Mercari, JPMC, and Venmo Platforms

TL;DR

This paper explores the use of temporal motifs to analyze financial transaction networks, demonstrating improved fraud detection and social relation predictions across Mercari, JPMC, and Venmo platforms.

Contribution

It introduces the application of temporal motifs to financial networks, showing their effectiveness in fraud detection and social relation analysis with practical runtime performance.

Findings

Temporal motifs outperform baseline methods in fraud detection.

Temporal motifs improve friendship prediction accuracy.

Analysis reveals patterns in rare temporal cycles.

Abstract

Understanding the dynamics of financial transactions among people is critical 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 to several baselines, including a previous method that considers simple graph features and two node embedding techniques (LINE and node2vec), while being practical in terms of runtime performance. 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, such as temporal cycles. We believe that the analysis, datasets, and lessons from this work will be beneficial for future research on financial transaction networks.