<p>Cryptocurrency represents a novel form of digital currency characterized by user anonymity and cryptographic security measures. However, the absence of a central authority renders cryptocurrency particularly susceptible to money laundering activities. Given that transaction data in cryptocurrency networks is organized in a chain-like structure, graph-based models have emerged as effective tools for detecting and analyzing such activities. Despite their efficacy, traditional graph-based models often lack transparency due to their black-box nature, making it challenging to understand the rationale behind their predictions. This limitation underscores the need for eXplainable Artificial Intelligence (XAI) methods, which aim to elucidate the decision-making process of complex models. In response to this need, paper endeavors to enhance model transparency in the context of cryptocurrency money laundering detection by applying XAI techniques. By leveraging XAI methods, such as Graph-based Local Interpretable Model-Agnostic Explanations (GraphLIME) and Graph Neural Network Explainer (GNNExplainer), the study seeks to provide interpretable explanations for predicted transactions, thereby shedding light on the underlying patterns and features driving illicit activities within cryptocurrency networks. Through this approach, the paper aims to contribute in the development of more transparent and accountable detection mechanisms for combating money laundering in the cryptocurrency domain.</p>