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

Belinda Ndlovu; Fungai Jacqueline Kiwa; Martin  Muduva; Colletor T. Chipfumbu; Sheltar  Marambi; Amazing  Maphosa

doi:10.47540/ijias.v6i1.2306

Authors

Belinda Ndlovu National University of Science and Technology, Zimbabwe
Fungai Jacqueline Kiwa Chinhoyi University of Technology, Zimbabwe
Martin Muduva Midlands State University, Zimbabwe
Colletor T. Chipfumbu Midlands State University, Zimbabwe
Sheltar Marambi Midlands State University, Zimbabwe
Amazing Maphosa National University of Science and Technology, Zimbabwe

DOI:

https://doi.org/10.47540/ijias.v6i1.2306

Keywords:

Anti-Money Laundering, Financial Crime Detection, Graph Convolutional Network, Machine Learning, Transaction Networks

Abstract

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.

References

Ahmad, N. (2024). Deep Learning for Fraud Detection in Financial Transactions: A Novel Approach to Detect Hidden Anomalies. Sep.

Akbar, S. F., Adams, R., Williams, L., & Lee, J. (2025). A Lightweight Graph Neural Network for Fraud Detection in Financial Transactions Graph Convolutional Networks (GCNs) are expensive and memory-intensive, making them unsuitable for transactions as an independent instance. This is a promising alternative — by model. 1(1), 76–93.

Alapati, N. K. (2024). Graph-based Semi-Supervised Learning for Fraud Detection in Finance Graph-based Semi-Supervised Learning for Fraud Detection in Finance. August.

Alarab, I., & Prakoonwit, S. (2023). Graph-Based LSTM for Anti-money Laundering: Experimenting Temporal Graph Convolutional Network with Bitcoin Data. Neural Processing Letters, 55(1), 689–707.

Alarab, I., & Prakoonwit, S. (2024). Robust recurrent graph convolutional network approach based on sequential prediction of illicit transactions in cryptocurrencies. Multimedia Tools and Applications, 83(20), 58449–58464.

Alarab, I., Prakoonwit, S., & Nacer, M. I. (2020). Competence of graph convolutional networks for AML in the Bitcoin blockchain. ACM International Conference Proceeding Series, 23–27.

Alarfaj, F. K., & Shahzadi, S. (2024). Enhancing Fraud Detection in Banking with Deep Learning: Graph Neural Networks and Autoencoders for Real-Time Credit Card Fraud Prevention. IEEE Access, August 2024.

Alawadhi, M. (2024). Money Laundering Transactions Chronology Analysis using Money Laundering Transactions Chronology Analysis using Artificial Intelligence.

Alenova, M., Utaliyeva, A., & Li, K. J. (2024). Detecting Hawala network for money laundering by graph mining. Journal of Finance and Data Science, 10(November), 100147.

Alharahsheh, H. H., & Pius, A. (2020). A review of key paradigms: Positivism VS interpretivism. Global Academic Journal of Humanities and Social Sciences, 2(3), 39–43.

Alharbi, A., & Alsubhi, K. (2021). Botnet Detection Approach Using Graph-Based Machine Learning. IEEE Access, 9, 99166–99180.

Al-mansoori, A. (2021). Graph Databases for Fraud Detection: A Fresh Look at Financial Security Abstract: 2(1), 1–9.

Assumpcao, H. S., Souza, F., Campos, L. L., De Castro Pires, V. T., De Almeida, P. M. L., & Murai, F. (2022). DELATOR: Money Laundering Detection via Multi-Task Learning on Large Transaction Graphs. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022, 709–714.

Avenue, S. (2025). Liberating the definition of work, None but ourselves. 686.

Bakhshinejad, N., Nguyen, U. T., Ghahremani, S., & Soltani, R. (2024). A Graph-Based Deep Learning Model for the Anti-Money Laundering Task of Transaction Monitoring. International Joint Conference on Computational Intelligence, 1(IJCCI), 496–507.

Bakhshinejad, N., Soltani, R., Nguyen, U. T., & Messina, P. (2022). A Survey of Machine Learning Based Anti-Money Laundering Solutions. Researchgate.Net, November. https://www.researchgate.net/profile/Nazanin-Bakhshinejad-2/publication/364326902_A_Survey_of_Machine_Learning_Based_Anti-Money_Laundering_Solutions/links/635748a68d4484154a30bfbb/A-Survey-of-Machine-Learning-Based-Anti-Money-Laundering-Solutions.pdf

Bellandi, V., Ceravolo, P., Maghool, S., & Siccardi, S. (2022). Graph embeddings in criminal investigation: towards combining precision, generalisation and transparency : Special issue on computational aspects of network science. World Wide Web, 25(6), 2379–2402.

Blanuša, J., Cravero Baraja, M., Anghel, A., Von Niederhäusern, L., Altman, E., Pozidis, H., & Atasu, K. (2024). Graph Feature Preprocessor: Real-time Subgraph-based Feature Extraction for Financial Crime Detection. ICAIF 2024 - 5th ACM International Conference on AI in Finance, 222–230.

Caglayan, M., & Bahtiyar, S. (2022). Money Laundering Detection with Node2Vec. Gazi University Journal of Science, 35(3), 854–873.

Cardoso, M., Saleiro, P., & Bizarro, P. (2022). LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering. In Proceedings of the 3rd ACM International Conference on AI in Finance, ICAIF 2022 (Vol. 1, Issue 1). Association for Computing Machinery.

Chege, K. A., & Otieno, O. C. (2020). Research Philosophy Design and Methodologies: A Systematic Review of Research Paradigms in Information Technology. 8(5), 33–38.

Cheng, D., Ye, Y., Xiang, S., Ma, Z., Zhang, Y., & Jiang, C. (2023). Anti-Money Laundering by Group-Aware Deep Graph Learning. IEEE Transactions on Knowledge and Data Engineering, 35(12), 12444–12457.

Chitimira, H. (2022). A regulatory analysis of digital financial services and the adoption of central bank digital currencies in Zimbabwe and South Africa. 1 financial services in many countries, including Zimbabwe and South Africa . 4 do not have access to useful, convenient an. https://doi.org/10.24818/TBJ/2023/13/3.04

Chuang, H. Y., & Chen, R. M. (2024). Feature Selection for Malicious Detection on Industrial IoT Using Machine Learning. Sensors and Materials, 36(3), 1035–1046.

D’oro, P., Nasca, E., Masci, J., & Matteucci, M. (2019). Group Anomaly Detection via Graph Autoencoders. NIPS Workshop, 1–8.

Daniel, G. V., Chandrasekaran, K., Meenakshi, V., & Paneer, P. (2023). Robust Graph Neural-Network-Based Encoder for Node and Edge Deep Anomaly Detection on Attributed Networks. Electronics (Switzerland), 12(6).

Deprez, B., Vanderschueren, T., Baesens, B., Verdonck, T., & Verbeke, W. (2024). Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation. http://arxiv.org/abs/2405.19383

Di Gennaro, M., Panebianco, F., Pianta, M., Zanero, S., & Carminati, M. (2024). Amatriciana: Exploiting Temporal GNNs for Robust and Efficient Money Laundering Detection. IEEE International Conference on Data Mining Workshops, ICDMW, MCC, 254–261.

Dong, Y., Yao, J., Wang, J., Liang, Y., Liao, S., & Xiao, M. (2024). Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networks. 2024 6th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2024, 818–823.

Dumitrescu, B., Baltoiu, A., & Budulan, S. (2022). Anomaly Detection in Graphs of Bank Transactions for Anti Money Laundering Applications. IEEE Access, 10, 47699–47714.

Eddin, A. N., Bono, J., Aparício, D., Polido, D., Ascensão, J. T., Bizarro, P., & Ribeiro, P. (2021). Anti-Money Laundering Alert Optimization Using Machine Learning with Graphs. http://arxiv.org/abs/2112.07508

Effendi, F., & Chattopadhyay, A. (2025). Privacy-Preserving Graph-Based Machine Learning with Fully Homomorphic Encryption for Collaborative Anti-money Laundering. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 15351 LNCS, 80–105.

Egressy, B., von Niederhäusern, L., Blanuša, J., Altman, E., Wattenhofer, R., & Atasu, K. (2024). Provably Powerful Graph Neural Networks for Directed Multigraphs. Proceedings of the AAAI Conference on Artificial Intelligence, 38(10), 11838–11846.

Fan, J., Shar, L. K., Zhang, R., Liu, Z., Yang, W., Niyato, D., Mao, B., & Lam, K.-Y. (2025). Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions. XX(Xx), 1–25. http://arxiv.org/abs/2503.10058

Fard, S. H. (2023). Machine Learning on Dynamic Graphs: A Survey on Applications. Proceedings - 2023 IEEE 9th Multimedia Big Data, BigMM 2023, 32–39.

Ferretti, S., D'Angelo, G., & Ghini, V. (2025). Enhancing Anti-Money Laundering Frameworks: An Application of Graph Neural Networks in Cryptocurrency Transaction Classification. IEEE Access, 13(January), 50201–50215.

Fontes, X., Aparício, D., Silva, M. I., Malveiro, B., Ascensão, J. T., & Bizarro, P. (2021). Finding NeMo: Fishing in banking networks using network motifs. CEUR Workshop Proceedings, 2929, 1–6.

Frumerie, R. (2021). Money Laundering Detection using Tree Boosting and Graph Learning Algorithms. Royal Institute of Technology School of Engineering Sciences.

Gaviyau, W., & Sibindi, A. B. (2023). Global Anti-Money Laundering and Combating Terrorism Financing Regulatory Framework: A Critique. Journal of Risk and Financial Management, 16(7).

Gounoue, S., Sao, A., & Gottschalk, S. (2025). TeMP-TraG: Edge-based Temporal Message Passing in Transaction Graphs. 2. http://arxiv.org/abs/2503.16901

Grzenda, A., Speier, W., Siddarth, P., Pant, A., Krause-Sorio, B., Narr, K., & Lavretsky, H. (2021). Machine Learning Prediction of Treatment Outcome in Late-Life Depression. Frontiers in Psychiatry, 12.

Han, J., Huang, Y., Liu, S., & Towey, K. (2020). Artificial intelligence for anti-money laundering: a review and extension. Digital Finance, 2(3–4), 211–239.

Hatam, M. A. (2024). Crime Rate Analysis and E-crime prevention in Dubai using machine learning . by A Thesis Submitted in Partial Fulfilment of the Requirements for the.

Hennink, M., & Kaiser, B. N. (2022). Sample sizes for saturation in qualitative research: A systematic review of empirical tests. Social Science and Medicine, 292, 114523.

Innan, N., Sawaika, A., Dhor, A., Dutta, S., Thota, S., Gokal, H., Patel, N., Khan, M. A. Z., Theodonis, I., & Bennai, M. (2024). Financial fraud detection using quantum graph neural networks. Quantum Machine Intelligence, 6(1).

Irshad, F., Alkhalifah, T., Alturise, F., & Khan, Y. D. (2024). GCF-MLD: Integrated Approach for Money Laundering Detection using Machine Learning and Graph Network Analysis. IEEE Access, 12(November), 183961–183972.

Japinye, A. O. (2024). Integrating Machine Learning in Anti-Money Laundering through Crypto: A Comprehensive Performance Review. European Journal of Accounting, Auditing and Finance Research, 12(4), 54–80.

Jensen, R. I. T., & Iosifidis, A. (2023). Fighting Money Laundering With Statistics and Machine Learning. IEEE Access, 11(January), 8889–8903.

Jiang, X., & Tsai, W. T. (2025). Directed Graph Neural Networks for Anomaly Detection of Smart Ponzi Schemes. IEEE Access, 13(April), 62367–62377.

Jullum, M., Løland, A., Huseby, R. B., Ånonsen, G., & Lorentzen, J. (2020). Detecting money laundering transactions with machine learning. Journal of Money Laundering Control, 23(1), 173–186.

Junjie, M., & Yingxin, M. (2022). The Discussions of Positivism and Interpretivism. Global Academic Journal of Humanities and Social Sciences, 4(1), 10–14.

Karim, M. R., Hermsen, F., Chala, S. A., de Perthuis, P., & Mandal, A. (2023). Catch Me If You Can: Semi-supervised Graph Learning for Spotting Money Laundering. 1–10. http://arxiv.org/abs/2302.11880

Karim, M. R., Hermsen, F., Chala, S. A., De Perthuis, P., & Mandal, A. (2024). Scalable Semi-Supervised Graph Learning Techniques for Anti Money Laundering. IEEE Access, 12(April), 50012–50029.

Ketenci, U. G., Kurt, T., Onal, S., Erbil, C., Akturkoglu, S., & Ilhan, H. S. (2021). A Time-Frequency Based Suspicious Activity Detection for Anti-Money Laundering. IEEE Access, 9, 59957–59967.

Khan, A., & Akcora, C. G. (2022). Graph-based Management and Mining of Blockchain Data. In International Conference on Information and Knowledge Management, Proceedings (Vol. 1, Issue 1). Association for Computing Machinery.

Kunci, K. et al. (2024) “The Adequacy of Cybersecurity in Financial Institutions in Zimbabwe,” International Research Journal of Business Studies [Preprint], (03).

Kute, D. V., Pradhan, B., Shukla, N., & Alamri, A. (2021). Deep Learning and Explainable Artificial Intelligence Techniques Applied for Detecting Money Laundering-A Critical Review. IEEE Access, 9, 82300–82317.

Labanca, D., Primerano, L., Markland-Montgomery, M., Polino, M., Carminati, M., & Zanero, S. (2022). Amaretto: An Active Learning Framework for Money Laundering Detection. IEEE Access, 10, 41720–41739.

Laundering, A., Pousette, T., & Rosendal, A. (2024). Explainable Anti-Money Laundering.

Lawal, K. (2025). AI for Anti-Money Laundering : Advanced Anomaly Detection in US Banking Systems Author: Kareem Lawal Date: 27 th March 2025 Abstract: March.

Li, R., Liu, Z., Ma, Y., Yang, D., & Sun, S. (2023). Internet Financial Fraud Detection Based on Graph Learning. IEEE Transactions on Computational Social Systems, 10(3), 1394–1401.

Li, X., Li, Y., Mo, X., Xiao, H., Shen, Y., & Chen, L. (2023). Diga: Guided Diffusion Model for Graph Recovery in Anti-Money Laundering. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Ml, 4404–4413.

Li, X., Liu, S., Li, Z., Han, X., Shi, C., Hooi, B., Huang, H., & Cheng, X. (2020). Flowscope: Spotting money laundering based on graphs. AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, 4731–4738.

Liu, W. (2025). Deep Reinforcement Learning with Graph Neural Networks for Financial Fraud Risk Mitigation. 1(1), 1–11.

Lo, W. W., Kulatilleke, G. K., Sarhan, M., Layeghy, S., & Portmann, M. (2023). Inspection-L: self-supervised GNN node embeddings for money laundering detection in Bitcoin. Applied Intelligence, 53(16), 19406–19417.

Lu, H., & Wang, H. (2024). Graph Contrastive Pre-training for Anti-money Laundering. International Journal of Computational Intelligence Systems, 17(1).

Mandela, S., Naga, R., Jagan, V., & Naik, M. C. (2023). Auction Algorithm : Peer-To-Peer System Based on Hybrid Technologies for Smallholder Farmers to Control Demand and Supply. January. https://doi.org/10.55529/ijrise.31.9.23

Martínez-Sánchez, J. F., Cruz-García, S., & Venegas-Martínez, F. (2020). Money laundering control in Mexico: A risk management approach through regression trees (data mining). Journal of Money Laundering Control, 23(2), 427–439.

Mayeni, R., Dube, S., Ndlovu, B., & Maduva, M. (2024). A Novel Ensemble-based Machine Learning Model for Anomaly Detection in CDRs to Identify International Revenue Share Fraud. https://doi.org/10.46254/EU07.20240070

Mellinger, C. D., & Hanson, T. A. (2020). Methodological considerations for survey research: Validity, reliability, and quantitative analysis. Linguistica Antverpiensia, New Series – Themes Translation Studies, 19, 172–190.

Mendonça, R., Salazar, A., & Martinez, E. (2025). Graph-Based Deep Learning for E-Commerce Fraud Detection. 2(1), 1–10.

Mnkandla, A. Z., Ndlovu, B., Dube, S., Nyoni, P., & Kiwa, F. J. (2024). Loan Eligibility System Using Machine Learning. https://doi.org/10.46254/EU07.20240079

Muminovic, A., & Halili, F. (2024). Money laundering prevention in the digital age: Leveraging graph databases for effective solutions. International Journal of Technical and Natural Sciences, 4(1), 1–10.

Navarro Cerdán, J. R., Millán Escrivá, D., Larroza, A., Pons-Suñer, P., & Pérez Cortés, J. C. (2023). A Deep GCN Approach Based on Multidimensional Projections and Classification to Cybercrime Detection in a True Imbalanced Problem with Semisupervision. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4519572

Naveed, N., Munawar, S., & Usman, A. (2023). Intelligent Anti-Money Laundering Fraud Control Using Graph-Based Machine Learning Model for the Financial Domain. Journal of Cases on Information Technology, 25(1), 1–20.

Plotnikova, V., Dumas, M., Nolte, A., & Milani, F. (2023). Designing a data mining process for the financial services domain ABSTRACT. Journal of Business Analytics, 6(2), 140–166.

Pocher, N., Zichichi, M., Merizzi, F., Shafiq, M. Z., & Ferretti, S. (2023). Detecting anomalous cryptocurrency transactions: An AML/CFT application of machine learning-based forensics. Electronic Markets, 33(1), 1–17.

Pourhabibi, T., Ong, K. L., Kam, B. H., & Boo, Y. L. (2020). Fraud detection: A systematic literature review of graph-based anomaly detection approaches. Decision Support Systems, 133(August 2019), 113303.

Renganathan, K. K., Karuppiah, J., Pathinathan, M., & Raghuraman, S. (2024). Credit card fraud detection with advanced graph based machine learning techniques. Indonesian Journal of Electrical Engineering and Computer Science, 35(3), 1963–1975.

Revesai, Z., Dzinomwa, M., Ndlovu, B., Nyoni, P., & Dube, S. (2023). Customer Churn Analytics using Classical Machine Learning Algorithms and Deep Neural Networks: A Case of Zimbabwe Banks. 2021, 60–70. https://doi.org/10.46254/ap04.20230040

Schmidt, J., Pasadakis, D., Sathe, M., & Schenk, O. (2024). GAMLNet: A graph based framework for the detection of money laundering. Proceedings - Swiss Conference on Data Science, SDS, 2024, 241–245.

Sciences, D. O. F. (2024). Financial Networks and Other Adventures in Graph Learning. 30422.

Shamina, S. V., Munister, V. D., Zolkin, A. L., Verbitskiy, R. A., & Dragulenko, V. V. (2021). Application of artificial intelligence and digital technologies in the organization of the educational process of specialists in the field of physics, engineering and metrology. Journal of Physics: Conference Series, 1889(2).

Silva, Í. D. G., Correia, L. H. A., & Maziero, E. G. (2023). Graph Neural Networks Applied to Money Laundering Detection in Intelligent Information Systems. ACM International Conference Proceeding Series, 252–259.

Solve fraud detection problem by using graph based learning methods. (2019). Journal of Engineering and Science Research, 3(4), 28–31.

Song, K., Dhraief, M. A., Xu, M., Cai, L., Chen, X., Mithal, A., & Chen, J. (2024). Identifying Money Laundering Subgraphs on the Blockchain. ICAIF 2024 - 5th ACM International Conference on AI in Finance, 195–203.

Starnini, M., Tsourakakis, C. E., Zamanipour, M., Panisson, A., Allasia, W., Fornasiero, M., Puma, L. L., Ricci, V., Ronchiadin, S., Ugrinoska, A., Varetto, M., & Moncalvo, D. (2021). Smurf-Based Anti-money Laundering in Time-Evolving Transaction Networks. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12978 LNAI, 171–186.

Sun, X., Feng, W., Liu, S., Xie, Y., Bhatia, S., Hooi, B., Wang, W., & Cheng, X. (2022). MonLAD: Money laundering agents detection in transaction streams. WSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining, 976–986.

Suzumura, T., Zhou, Y., Baracaldo, N., Ye, G., Houck, K., Kawahara, R., Anwar, A., Stavarache, L. L., Watanabe, Y., Loyola, P., Klyashtorny, D., Ludwig, H., & Bhaskaran, K. (2019). Towards Federated Graph Learning for Collaborative Financial Crimes Detection. 1–10.

Tang, Z., E, H., Sun, M., Zhao, L., Wang, R., & Song, M. (2023). Anti-money laundering method based on hierarchical risk control knowledge graph. 12803(Aics), 44.

Vallarino, D. (2025). AI-Powered Fraud Detection in Financial Services: GNN, Compliance Challenges, and Risk Mitigation.

Wang, Q., Tsai, W. T., & Du, B. (2025). RMGANets: reinforcement learning-enhanced multi-relational attention graph-aware network for anti-money laundering detection. Complex and Intelligent Systems, 11(1), 1–17.

Wang, S., Wang, P., Wu, B., Zhu, Y., Luo, W., & Pan, Y. (2024). Structural entropy minimization combining graph representation for money laundering identification. International Journal of Machine Learning and Cybernetics, 15(9), 3951–3968.

Weber, M., Domeniconi, G., Chen, J., Weidele, D. K. I., Bellei, C., Robinson, T., & Leiserson, C. E. (2019). Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics. 10.

Wójcik, F. (2024). An Analysis of Novel Money Laundering Data Using Heterogeneous Graph Isomorphism Networks. FinCEN Files Case Study. Econometrics, 28(2), 32–49.

Xu, H., Yu, K., Wei, M., & Zhu, Y. (2024). Intelligent Anti-Money Laundering Transaction Pattern Recognition System Based on Graph Neural Networks. 2(1), 93–108.

Yu, Q., Ke, Z., Xiong, G., Cheng, Y., & Guo, X. (2024). Identifying Money Laundering Risks in Digital Asset Transactions Based on AI Algorithms. 2024 4th International Conference on Electronic Information Engineering and Computer Communication, EIECC 2024, 1081–1085.

Zade, N. P. (2024). Exploring Graph-Based Machine Learning Techniques for Transaction Fraud Detection: A Comparative Analysis of Performance.

Zhang, H. (2025). Research on the Application of Knowledge Graphs in Bank Risk Management. 0, 62–69.

Zhang, Q., Zhu, Y., Zhang, R., Chen, R., & Lan, T. (2025). Research on Anti-Money Laundering Technology Based on Graph Attention Mechanism. 13511, 1–11.