Anti-money laundering is an international web of laws, regulations, and procedures aimed at uncovering money that has been disguised as legitimate income. Strict anti-money laundering (AML) laws and procedures require major and continuous transaction observation in inferring possible illegal events. Nevertheless, traditional rule-based approaches in banks frequently generate a significant number of false positives, which impose a major burden. In this case, deep learning approaches, especially graph-based Graph Neural Network-based (GNN) methods, could be explored in generating better anti-money laundering results. Here, we propose a diffusion-based AMLPD, which is novel in generating unsupervised node embeddings via learning graph embeddings inductively while detecting AML. AMLPD assumes a direction between edges, and it incorporates vertex and edge feature knowledge while encoding graph’s structure knowledge. AMLPD infers a vertex’s local state via combining diffusion with PageRank, which is an important knowledge for AML when embedded into low dimensional space Then, our approach can detect AMLs by a classifier using this low dimensional representation. Our approach can be scaled to larger data, as well as it can help with explainable AI by facilitating the embeddings analysis. According to experiments, our approach outperforms the baseline approaches. Therefore, AMLPD is favourable in enhancing the quality of GNN-based AML identification.