In this thesis, we propose an iterative graph coarsening-learning framework to detect money laundering gangs in bank transaction networks. Instead of classifying individual accounts, our goal is to identify groups of accounts that act together to hide illicit activities. We begin by providing a mathematical definition of gangs in the context of transaction graphs, modeling them as dense and highly interactive subgraphs that share structural and behavioral similarities, such as transaction patterns. The proposed method progressively reduces the complexity of the transaction graph through embedding-based coarsening, where nodes with similar structural and semantic patterns are merged into super-nodes representing potential groups.