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.