CuRL: Coupled Representation Learning of Cards and Merchants to Detect Transaction Frauds

Published in 30th International Conference on Artificial Neural Networks (ICANN), 2021

Payment networks like Mastercard or Visa process billions of transactions every year. A significant number of these transactions are fraudulent that cause huge losses to financial institutions. Conventional fraud detection methods fail to capture higher-order interactions between payment entities i.e., cards and merchants, which could be crucial to detect out-of-pattern, possibly fraudulent transactions. Several works have focused on capturing these interactions by representing the transaction data either as a bipartite graph or homogeneous graph projections of the payment entities. In a homogeneous graph, higher-order cross-interactions between the entities are lost and hence the representations learned are sub-optimal. In a bipartite graph, the sequences generated through random walk are stochastic, computationally expensive to generate, and sometimes drift away to include uncorrelated nodes. Moreover, scaling graph-learning algorithms and using them for real-time fraud scoring is an open challenge.

In this paper, we propose CuRL and tCuRL, coupled representation learning methods that can effectively capture the higher-order interactions in a bipartite graph of payment entities. Instead of relying on random walks, proposed methods generate coupled session-based interaction pairs of entities which are then fed as input to the skip-gram model to learn entity representations. The model learns the representations for both entities simultaneously and in the same embedding space, which helps to capture their cross-interactions effectively. Furthermore, considering the session constrained neighborhood structure of an entity makes the pair generation process efficient. This paper demonstrates that the proposed methods run faster than many state-of-the-art representation learning algorithms and produce embeddings that outperform other relevant baselines on fraud classification task.

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Cite this paper as:
Gramopadhye M. et al. (2021) CuRL: Coupled Representation Learning of Cards and Merchants to Detect Transaction Frauds. In: Farkaš I., Masulli P., Otte S., Wermter S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science, vol 12895. Springer, Cham.