Developing a graph-based machine learning model for identifying money laundering networks associated with sanctioned entities in a bank in Zimbabwe

Money laundering networks associated with sanctioned entities pose a significant risk to financial systems, often operating through complex relational transaction structures that evade traditional rule-based monitoring. While graph neural networks have demonstrated promise in financial crime detection, limited work has formally modelled sanction-linked transaction networks within highly imbalanced banking datasets under consistent comparative evaluation. This study proposes a directed weighted graph-based learning framework for identifying sanction-associated money laundering networks using real-world banking transaction data. Transactions were modelled as relational graphs, with accounts as nodes and transfers as weighted edges, and evaluated using a Graph Convolutional Network (GCN) against classical and ensemble classifiers. The proposed model achieved an accuracy of 88.18%, F1-score of 0.7345, ROC-AUC of 0.8968, and a superior Matthews Correlation Coefficient compared to baseline methods. Results demonstrate that relational graph modelling improves the detection of structurally coordinated laundering behaviours that are not captured by independent transaction classifiers. These findings support the integration of graph neural network architectures into anti-money laundering systems to enhance sanction-linked detection capabilities in complex financial networks.