Money laundering seriously threatens economic stability by legitimizing illegal gains. Despite the transparency and security advantages offered by the blockchain technology, anonymity can create a platform for concealing illegal activities. Therefore, detecting and preventing money laundering activities in blockchain networks are of great importance. This study classifies money transfers during Bitcoin transactions as licit or illicit. By working on the Elliptic dataset to detect money laundering activities in the Bitcoin network, we examined money laundering traffic data using a graph data structure. This study presents a novel method for analyzing complex networks in money laundering as a chaotic time series. First, we increase the number of features of the graph nodes and convert them into a time series. By transferring the obtained time series to the phase space, we calculated the Lyapunov Exponents and aimed to capture the changes and uncertainties in the dynamic structure of the system more accurately using different embedding dimensions. We reconstructed the graph structure representing the transactions based on the feature vectors of these exponents, and classified the transactions using the Graph Convolutional Network method. In our study, we achieved a precision of 92.5%, recall of 92.1%, F1-score of 92.3%, and accuracy of 86.2%. These results demonstrate the effectiveness and reliability of our model in detecting money laundering. This study offers a novel approach for classifying chaotic structures in anti money laundering.

Money laundering, Lyapunov exponents, Chaos theory, Graph convolution network, Classification