Temporal Motifs for Financial Networks: A Study on Mercari, JPMC, and Venmo Platforms
This work addresses the need for improved analysis of temporal dynamics in financial networks for fraud detection and social prediction, though it is incremental as it applies an existing concept (temporal motifs) to new financial datasets.
The authors tackled the problem of analyzing financial transaction networks for applications like fraud detection and social relation prediction by applying temporal motifs, which capture the order and repetition of transactions within short time periods. They demonstrated superior performance over baselines, achieving better results in fraud detection on Mercari and J.P. Morgan Chase networks and in friendship prediction on Venmo, with high accuracy in vendor identification.
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.