Digital Transformation of Anti-Money Laundering Business Processes: A Capability-Oriented Perspective on Analytics-Driven AML
By: Ajša Lutar and Simona Sternad Zabukovšek
References
- Ahmad, T., & Van Looy, A. (2020). Business process management and digital innovations: A systematic literature review. Sustainability, 12(17), 6827. https://doi.org/10.3390/su12176827
- Alnasser, A., Almarri, M., Alhindi, A., Almansoori, A., Alshamsi, A., Alhashmi, S., & Salloum, S. A. (2020). Anti-money laundering systems: A systematic literature review. Journal of Money Laundering Control, 24(2), 249–267. https://doi.org/10.1108/JMLC-02-2020-0018
- Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. (2013). Digital business strategy: Toward a next generation of insights. MIS Quarterly, 37(2), 471–482. https://doi.org/10.25300/MISQ/2013/37:2.3
- Chai, Z., Yang, Y., Dan, J., Tian, S., Meng, C., Wang, W., & Sun, Y. (2023). Towards learning to discover money laundering subnetwork in massive transaction network. Proceedings of the AAAI Conference on Artificial Intelligence, 37(12), 14154–14162. https://doi.org/10.1609/aaai.v37i12.26656
- Colladon, A. F., & Remondi, E. (2017). Using social network analysis to prevent money laundering. Expert Systems with Applications, 67, 49–58. https://doi.org/10.1016/j.eswa.2016.09.029
- Cornelli, G., Frost, J., Gambacorta, L., Rau, R., Wardrop, R., & Ziegler, T. (Eds.). (2021). Fintech and the digital transformation of financial services (BIS Papers No. 117). Bank for International Settlements. https://www.bis.org/publ/bppdf/bispap117.htm
- Davenport, T. H., & Harris, J. G. (2017). Competing on analytics: The new science of winning (Updated ed.). Harvard Business Review Press.
- Deprez, B., Vanderschueren, T., Baesens, B., Verdonck, T., & Verbeke, W. (2025). Network analytics for anti-money laundering: A systematic literature review and experimental evaluation. INFORMS Journal on Data Science, 5(2), 119–154. https://doi.org/10.1287/ijds.2024.0042
- Dumas, M., La Rosa, M., Mendling, J., & Reijers, H. A. (2023). Fundamentals of business process management (3rd ed.). Springer. https://doi.org/10.1007/978-3-662-67541-4
- Ellström, D., Holtström, J., Berg, E., & Josefsson, C. (2022). Dynamic capabilities for digital transformation. Journal of Strategy and Management, 15(2), 272–286. https://doi.org/10.1108/JSMA-04-2021-0089
- Financial Action Task Force. (2023). International standards on combating money laundering and the financing of terrorism & proliferation: The FATF recommendations. https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Fatf-recommendations.html
- Halford, E., Gibson, I., Newfield, M., & Dhanwala, M. (2025). Developing a scoring model for managing money laundering transactions using machine learning. Journal of Money Laundering Control, 28 (7), 30–49. https://doi.org/10.1108/JMLC-09-2024-0152
- Johannessen, F., & Jullum, M. (2025). Finding money launderers using heterogeneous graph neural networks. The Journal of Finance and Data Science, 11, 100175. https://doi.org/10.1016/j.jfds.2025.100175
- Korherr, P., Kanbach, D. K., Kraus, S., & Mikalef, P. (2022). From intuitive to data-driven decision-making in digital transformation. Digital Business, 2(2), 100045. https://doi.org/10.1016/j.digbus.2022.100045
- Oliveira, R. M. A., Sant’Anna, A. M. O., & Ferreira, P. H. (2025). Complex networks-based anomaly detection for financial transactions in anti-money laundering. Forensic Science International: Digital Investigation, 55, 302005. https://doi.org/10.1016/j.fsidi.2025.302005
- Oztas, B., Cetinkaya, D., Adedoyin, F., Budka, M., Aksu, G., & Dogan, H. (2024). Transaction monitoring in anti-money laundering: A qualitative analysis and points of view from industry. Future Generation Computer Systems, 159, 161–171. https://doi.org/10.1016/j.future.2024.05.027
- Papathomas, A., & Konteos, G. (2024). Financial institutions digital transformation: The stages of the journey and business metrics to follow. Journal of Financial Services Marketing, 29(2), 590–606. https://doi.org/10.1057/s41264-023-00223-x
- Sharda, R., Delen, D., & Turban, E. (2023). Analytics, data science, & artificial intelligence: Systems for decision support (12th ed.). Pearson.
- Tiwari, M., Ferrill, J., & Mehrotra, V. (2023). Using graph database platforms to fight money laundering: Advocating large scale adoption. Journal of Money Laundering Control, 26(3), 474–487. https://doi.org/10.1108/JMLC-03-2022-0047
- Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118–144. https://doi.org/10.1016/j.jsis.2019.01.003
- Warner, K. S. R., & Wäger, M. (2019). Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Planning, 52(3), 326–349. https://doi.org/10.1016/j.lrp.2018.12.001
- 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. arXiv. https://arxiv.org/abs/1908.02591
Language: English
Page range: 113 - 136
Submitted on: Jul 1, 2026
Accepted on: Aug 1, 2026
Published online: Oct 8, 2026
Published in print: Sep 1, 2026
Published by: University of Maribor
In partnership with: Paradigm Publishing Services
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© 2026 Ajša Lutar, Simona Sternad Zabukovšek, published by University of Maribor
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.