Digital Transformation of Anti-Money Laundering Business Processes: A Capability-Oriented Perspective on Analytics-Driven AML

Avtorji

  • Ajša Lutar
  • Simona Sternad Zabukovšek

Ključne besede:

Anti-money laundering (AML), Digital transformation, Business process management (BPM), Business analytics, Organisational analytical capabilities, Analytics-driven AML

Povzetek

Anti-money laundering (AML) is becoming an increasingly information-intensive business process requiring advanced analytical support for effective organisational decision-making. Although previous research has extensively investigated machine learning, graph analytics, and other analytical methods, existing studies largely examine these approaches as isolated technological solutions. This conceptual study adopts a business process perspective. It develops a capability-oriented framework for analytics-driven AML by synthesizing digital transformation, business process management, business analytics, and AML literature. This perspective conceptualises the digital transformation of AML as the coordinated development of interconnected organisational analytical capabilities embedded within business processes. The study contributes by integrating previously fragmented research streams into a coherent, management-oriented perspective. It provides a conceptual foundation for future empirical research and practical guidance for financial institutions pursuing analytics-driven AML transformation.

Prenosi

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Literatura

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 sub-network 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

Objavljeno

28.09.2026

Izjava o dostopnosti podatkov

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Kako citirati

Lutar, A., & Sternad Zabukovšek, S. (2026). Digital Transformation of Anti-Money Laundering Business Processes: A Capability-Oriented Perspective on Analytics-Driven AML. Naše gospodarstvo/Our economy, 72(3), 113-136. https://journals.um.si/index.php/oe/article/view/6389