Abstract
Context: Money Laundering (ML) is a global crime that has a close relation with other crimes, such as: illegal drug trading, terrorism or arms trafficking. Criminals in today’s technology-driven society use every means available at their disposal to launder the profit made from their illegal activities. In response, international anti-money laundering (AML) efforts are made with AML systems. Objective: Identify and systematize the approaches, techniques and algorithms used in Computer Science (CS) to fight ML, besides identifying the trends in the field. Method: A systematic literature mapping was conducted to analyze the scientific research in the field. Results: The main approaches were identified, supervised classifiers and clusters, along with the trend of papers published over the years. China was the country with the highest number of published papers. Conclusion: The most relevant studies in such research line adopt data mining and machine learning techniques using clusters and classifiers. The state of the art was mapped, making it clear that it is an area of interest for researchers around the world with growth potential. We believe that this work is relevant to the academy, governments and the community at large, presenting them with trends in the detection of money laundering.
This work was conducted during a scholarship supported by FAPITEC/CAPES.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lv, L.-T., Ji, N., Zhang, J.-L.: A RBF neural network model for anti-money laundering. In: International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR’08, vol. 1, pp. 209–215. IEEE (2008)
Zhang, Z.M., Salerno, J.J., Yu, P.S.: Applying data mining in investigating money laundering crimes. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 747–752. ACM (2003)
Schott, P.A.: Reference Guide to Anti-money Laundering and Combating the Financing of Terrorism. World Bank Publications, Washington, DC (2006)
Gao, S., Xu, D.: Conceptual modeling and development of an intelligent agent-assisted decision support system for anti-money laundering. Exp Syst Appl 36(2), 1493–1504 (2009)
Alexandre, C., Balsa, J.: Integrating client profiling in an anti-money laundering multi-agent based system. In: Rocha, A., Correia, A.M., Adeli, H., Reis, L.P., Teixeira, M.M. (eds.) New Advances in Information Systems and Technologies, pp. 931–941. Springer, Cham (2016)
Luo, X.: Suspicious transaction detection for anti-money laundering. Int. J. Secur. Its Appl. 8, 157–166 (2014)
Le Khac, N.A., Markos, S., Kechadi, M.-T.: A data mining-based solution for detecting suspicious money laundering cases in an investment bank. In: 2010 Second International Conference on Advances in Databases Knowledge and Data Applications (DBKDA), pp. 235–240. IEEE (2010)
Ju, C., Zheng, L.: Research on suspicious financial transactions recognition based on privacy-preserving of classification algorithm. In: First International Workshop on Education Technology and Computer Science, ETCS’09, vol. 2, pp. 525–528. IEEE (2009)
George, I., Kavakli, M.: Data mining in the investigation of money laundering and terrorist financing. In: Surveillance Technologies and Early Warning Systems: Data Mining Applications for Risk Detection, p. 228 (2010)
Freedman, R.S., Sobkowski, I.: Surveillance of parimutuel wagering integrity using expert systems and machine learning. In: IAAI (2010)
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)
Wang, S.-N., Yang, J.-G.: A money laundering risk evaluation method based on decision tree. In: 2007 International Conference on Machine Learning and Cybernetics, vol. 1, pp. 283–286. IEEE (2007)
Kingdon, J.: Ai fights money laundering. IEEE Intell. Syst. 19(3), 87–89 (2004)
Keyan, L., Tingting, Y.: An improved support-vector network model for anti-money laundering. In: 2011 Fifth International Conference on Management of e-Commerce and e-Government (ICMeCG), pp. 193–196. IEEE (2011)
Le Khac, N.A., Kechadi, M.-T.: Application of data mining for anti-money laundering detection: a case study. In: 2010 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 577–584. IEEE (2010)
Tang, J., Yin, J.: Developing an intelligent data discriminating system of anti-money laundering based on SVM. In: Proceedings of 2005 International Conference on Machine Learning and Cybernetics, 2005, vol. 6, pp. 3453–3457. IEEE (2005)
Liu, R., Qian, X.-L., Mao, S., Zhu, S.-Z.: Research on anti-money laundering based on core decision tree algorithm. In: Control and Decision Conference (CCDC), 2011 Chinese, pp. 4322–4325. IEEE (2011)
Paula, E.L., Ladeira, M., Carvalho, R.N., Marzagão, T.: Deep learning anomaly detection as support fraud investigation in Brazilian exports and anti-money laundering. In: 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 954–960. IEEE (2016)
Cao, D.K., Do, P.: Applying data mining in money laundering detection for the Vietnamese banking industry. In: Asian Conference on Intelligent Information and Database Systems, pp. 207–216. Springer (2012)
Yang, Y., Lian, B., Li, L., Chen, C., Li, P.: DBSCAN clustering algorithm applied to identify suspicious financial transactions. In: 2014 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), pp. 60–65. IEEE (2014)
Wang, X., Dong, G.: Research on money laundering detection based on improved minimum spanning tree clustering and its application. In: Second International Symposium on Knowledge Acquisition and Modeling, KAM’09, vol. 2, pp. 62–64. IEEE (2009)
Gao, Z.: Application of cluster-based local outlier factor algorithm in anti-money laundering. In: International Conference on Management and Service Science, MASS’09, pp. 1–4. IEEE (2009)
Cheong, T.-M., Si, Y.-W.: Event-based approach to money laundering data analysis and visualization. In: Proceedings of the 3rd International Symposium on Visual Information Communication, p. 21. ACM (2010)
Umadevi, P., Divya, E.: Money laundering detection using TFA system (2012)
Chen, Z., Nazir, A., Teoh, E.N., Karupiah, E.K., et al.: Exploration of the effectiveness of expectation maximization algorithm for suspicious transaction detection in anti-money laundering. In: 2014 IEEE Conference on Open Systems (ICOS), pp. 145–149. IEEE (2014)
Ngai, E., Hu, Y., Wong, Y., Chen, Y., Sun, X.: The application of data mining techniques in financial fraud detection: a classification framework and an academic review of literature. Decis. Support Syst. 50(3), 559–569 (2011)
Yue, D., Wu, X., Wang, Y., Li, Y., Chu, C.-H.: A review of data mining-based financial fraud detection research. In: International Conference on Wireless Communications, Networking and Mobile Computing, WiCom 2007, pp. 5519–5522. IEEE (2007)
Kitchenham, B.: Procedures for performing systematic reviews. Keele, UK, Keele University, vol. 33, no. 2004, pp. 1–26 (2004)
Petersen, K., Feldt, R., Mujtaba, S., Mattsson, M.: Systematic mapping studies in software engineering. In: EASE, vol. 8, pp. 68–77 (2008)
Brereton, P., Kitchenham, B.A., Budgen, D., Turner, M., Khalil, M.: Lessons from applying the systematic literature review process within the software engineering domain. J. Syst. Softw. 80(4), 571–583 (2007)
Wohlin, C., Runeson, P., Neto, P.A.d.M.S., Engström, E., do Carmo Machado, I., De Almeida, E.S.: On the reliability of mapping studies in software engineering. J. Syst. Soft. 86(10), 2594–2610 (2013)
Liu, X., Zhang, P., Zeng, D.: Sequence matching for suspicious activity detection in anti-money laundering. In: Intelligence and Security Informatics, pp. 50–61 (2008)
Elsevier, B.: Scopus Content Coverage Guide. Elsevier. Available at: https://www.elsevier.com/solutions/scopus/how-scopus-works/content (2017). Accessed Feb 2018
Zhu, T.: An outlier detection model based on cross datasets comparison for financial surveillance. In: IEEE Asia-Pacific Conference on Services Computing, APSCC’06, pp. 601–604. IEEE (2006)
Jun, T.: A cross datasets referring outlier detection model applied to suspicious financial transaction discrimination. In: Intelligence and Security Informatics, pp. 58–65. Springer, Berlin (2006)
Li, Y., Duan, D., Hu, G., Lu, Z.: Discovering hidden group in financial transaction network using hidden markov model and genetic algorithm. In: Sixth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD’09, vol. 5, pp. 253–258. IEEE (2009)
Camino, R.D., State, R., Montero, L., Valtchev, P.: Finding suspicious activities in financial transactions and distributed ledgers. In: 2017 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 787–796. IEEE (2017)
Dreżewski, R., Dziuban, G., Hernik, Ł., P ączek, M.: Comparison of data mining techniques for money laundering detection system. In: 2015 International Conference on Science in Information Technology (ICSITech), pp. 5–10. IEEE (2015)
Krishnapriya, G., Prabakaran, M.: Money laundering analysis based on time variant behavioral transaction patterns using data mining. J. Theor. Appl. Inf. Technol. 67(1), 12–17 (2014)
Liu, X., Zhang, P.: A scan statistics based suspicious transactions detection model for anti-money laundering (AML) in financial institutions. In: 2010 International Conference on Multimedia Communications (Mediacom), pp. 210–213. IEEE (2010)
Michalak, K., Korczak, J.: Graph mining approach to suspicious transaction detection. In: 2011 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 69–75. IEEE (2011)
Li, X., Cao, X., Qiu, X., Zhao, J., Zheng, J.: Intelligent anti-money laundering solution based upon novel community detection in massive transaction networks on spark. In: 2017 Fifth International Conference on Advanced Cloud and Big Data (CBD), pp. 176–181. IEEE (2017)
Gao, S., Xu, D., Wang, H., Wang, Y.: Intelligent anti-money laundering system. In: IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI’06, pp. 851–856. IEEE (2006)
Alexandre, C., Balsa, J.: A multiagent based approach to money laundering detection and prevention. In: ICAART (1), pp. 230–235 (2015)
Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining, 1st edn. Pearson Addison Wesley, Boston (2005)
Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, San Diego (2011)
Reddy, Y.B.: Event-based anomalies in big data. In: Information Technology-New Generations, pp. 33–42. Springer (2018)
Lin, R.A.K.-l., Shim, H.S.S.K.: Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In: Proceedings of the 21st International Conference on Very Large Data Bases, pp. 490–501. Citeseer (1995)
Keidel, A.: China’s financial sector: contributions to growth and downside risks. In: Barth, J.R., Tatom, J.A. (eds.) China’s Emerging Financial Markets, pp. 111–125. Springer, New York (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Barroso, B.L.K., Mangueira, F., Júnior, M.C. (2019). Fighting Against Money Laundering: A Systematic Mapping. In: Latifi, S. (eds) 16th International Conference on Information Technology-New Generations (ITNG 2019). Advances in Intelligent Systems and Computing, vol 800. Springer, Cham. https://doi.org/10.1007/978-3-030-14070-0_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-14070-0_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-14069-4
Online ISBN: 978-3-030-14070-0
eBook Packages: EngineeringEngineering (R0)