Implementation of a Web-Based System for money laundering detection and analysis using graphs
DOI:
https://doi.org/10.33064/iycuaa2026988405Keywords:
Money laundering, mathematical models, graph algorithms, web 2.0, database, assetsAbstract
Money laundering is a financial crime that involves concealing the origin of funds obtained through illicit activities such as corruption, organized crime, fraud, human trafficking, tax evasion, and the misuse of public resources. In 2021, the Financial Intelligence Unit reported 172,329 unusual transaction reports from financial institutions, associated with behaviors that do not match customer profiles and may be linked to money laundering activities. In response to this issue, this study proposes the development and implementation of a web-based system built on a microservices architecture and supported by a Neo4j graph-oriented database. The system integrates centrality, community, and closeness algorithms to analyze large volumes of financial data and identify relevant patterns and relationships. By enabling the integration of multiple information sources, the proposed solution improves the efficiency of data analysis and supports governmental entities in the detection, investigation, and monitoring of suspicious financial activities, in accordance with typologies established by...
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• 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
• 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
• Unidad de Inteligencia Financiera. (2023, diciembre 7). La Unidad de Inteligencia Financiera presenta la Evaluación Nacional de Riesgos de Lavado de Dinero y Financiamiento al Terrorismo 2023 (Comunicado No. 11/2023). Gobierno de México
• Lima, C. R. C., Carr, C. N., Margarido, J. J. P., & Silva, R. D. (2023). The incremental model in software development: A structured and interactive way to deliver quality products. Research, Society and Development, 12(4), e7512440934. https://doi.org/10.33448/rsd-v12i4.40934
• Reddy, A. S. (2024). A systematic review of software development methodologies and their application in Agile and DevOps environments. Journal of Recent Trends in Computer Science and Engineering, 12(5), 50–54. https://jrtcse.com/index.php/home/article/view/JRTCSE.2024.5.7
• Bueno, L. I. L. (2022). Un lenguaje de modelo de objetos. Dialnet, 7 (1), 25–29.
• Meza, R. A. (2024). Formal thinking, a determining factor in the teaching of class design under UML in programming. Revistas Milpa Alta TecNM, 7 (2).
• Tuyishime, A., Basciani, F., Cánovas Izquierdo, J. L., & Iovino, L. (2024). Dynamic Provisioning of REST APIs for Model Management. arXiv. https://arxiv.org/abs/2406.17176.
• Díaz, M. A., & Grass, B. D. (2022). A new framework for centrality on multilayer networks. Revista Cubana de Ciencias Informáticas.
• Nunes, M., Silva, T., & Lopes, A. (2023). Community detection in complex networks using the Louvain algorithm: Applications and performance analysis. Physica A: Statistical Mechanics and its Applications, 625, 128998. https://doi.org/10.1016/j.physa.2023.128998
• Taibi, D., & Lenarduzzi, V. (2023). Microservices Architecture: Principles, Benefits and Challenges in Modern Software Systems. IEEE Access, 11, 74215–74230. https://doi.org/10.1109/ACCESS.2023.3291456/
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Copyright (c) 2026 Francisco Cervando Lozano-Lozano, Andrés Salas-Núñez, Luis Eduardo Torres-Hernández, Alfredo Garcia-Castañón, Luis Enrique Márquez-Martínez
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