Abstract
Mobile money transfer services (MMTS) are currently being deployed in many markets across the world and are widely used for domestic and international remittances. However, they can be used for money laundering and other illegal financial operations. The paper considers an interactive multi-view approach that allows describing metaphorically the behavior of MMTS subscribers according to their transaction activities. The suggested visual representation of the MMTS users’ behavior based on the RadViz visualization technique helps to identify groups with similar behavior and outliers. We describe several case studies corresponding to the money laundering and behavioral fraud. They are used to assess the efficiency of the proposed a pproach as well as present and discuss the results of experiments.
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Achemlal, M., et al.: Scenario requirements. Technical report. MASSIF FP7-257475 project (2011)
Al-Khatib, A.: Electronic Payment Fraud Detection Techniques. World of Computer Science and Information Technology Journal (WCSIT) 2, 137–141 (2012)
Ankerst, M., Berchtold, S., Keim, D.A.: Similarity Clustering of Dimensions for an Enhanced Visualization of Multidimensional Data. In: 1998 IEEE Symposium on Information Visualization (INFOVIS 1998), pp. 52–60. IEEE Computer Society (1998)
Chang, R., Ghoniem, M., Kosara, R., Ribarsky, W., Yang, J., Suma, E., Ziemkiewicz, C., Kern, D., Sudjianto, A.: WireVis: Visualization of Categorical, Time-Varying Data From Financial Transactions. In: IEEE Symposium on Visual Analytics Science and Technology (VAST 2007), pp. 155–162 (2007)
ColorBrew2, http://colorbrewer2.org
Delloite. Visual Analytics: Revealing Corruption, Fraud, Waste, and Abuse. Presentation of the Forensic Center, http://www.slideshare.net/DeloitteForensicCenter/visual-analytics-revealing-corruption-fraud-waste-and-abuse-13958016
Di Caro, L., Frias-Martinez, V., Frias-Martinez, E.: Analyzing the Role of Dimension Arrangement for Data Visualization in Radviz. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds.) PAKDD 2010. LNCS, vol. 6119, pp. 125–132. Springer, Heidelberg (2010)
Fiserv. Financial Crime Risk Management solution, http://www.fiserv.com/risk-compliance/financial-crime-risk-management.htm
Gaber, C., Hemery, B., Achemlal, M., Pasquet, M., Urien, P.: Synthetic logs generator for fraud detection in mobile transfer services. In: Int. Conference on Collaboration Technologies and Systems (CTS 2013), pp. 174–179 (2013)
Jack, W., Tavneet, S., Townsend, R.: Monetary Theory and Electronic Money: Reflections on the Kenyan Experience. Economic Quarterly 96-1, 83–122 (2010)
Keim, D.A., Andrienko, G., Fekete, J.-D., Görg, C., Kohlhammer, J., Melançon, G.: Visual Analytics: Definition, Process, and Challenges. In: Kerren, A., Stasko, J.T., Fekete, J.-D., North, C. (eds.) Information Visualization. LNCS, vol. 4950, pp. 154–175. Springer, Heidelberg (2008)
Korczak, J., Łuszczyk, W.: Visual Exploration of Cash Flow Chains. In: The Federated Conference on Computer Science and Information Systems, pp. 41–46 (2011)
Kotenko, I., Novikova, E.: VisSecAnalyzer: A Visual Analytics Tool for Network Security Assessment. In: Cuzzocrea, A., Kittl, C., Simos, D.E., Weippl, E., Xu, L. (eds.) CD-ARES Workshops 2013. LNCS, vol. 8128, pp. 345–360. Springer, Heidelberg (2013)
Lin, L., Cao, L., Zhang, C.: The fish-eye visualization of foreign currency exchange data streams. In: Asia-Pacific Symposium on Information Visualisation, pp. 91–96 (2005)
Marghescu, D.: Multidimensional Data Visualization Techniques for Financial Performance Data: A Review, TUCS Technical Report No 810, University of Turku, Finland (2007)
Merrit, C.: Mobile Money Transfer Services: The Next Phase in the Evolution in Person-to-Person Payments. Technical report. Retail Payments Risk Forum (2010)
Money Laundering and Terrorist Financing Trends in FINTRAC Cases Disclosed Between 2007 and 2011. FINTRAC Typologies and Trends Reports (2012)
Money Laundering using New Payment Methods. FATF Report (2010)
Neural-technologies. MinotaurTM Fraud Detection Software - Finance Sector, http://www.neuralt.com/fraud_detection_software.html
Nice Actimize Integrated Fraud Management, http://www.niceactimize.com/index.aspx?page=solutionsfraud
Novikova, E., Kotenko, I.: Analytical Visualization Techniques for Security Information and Event Management. In: 21st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP 2013), pp. 519–525. IEEE Computer Society, Belfast (2013)
Okutyi, E.: Safaricom tightens security on M-Pesa with fraud management system (2012), http://www.humanipo.com/news/1341/Safaricom-tightens-security-on-M-Pesa-with-Fraud-Management-system
Orange Money dépasse les 4 millions de clients et lance ses services en Jordanie et à Maurice (2012) (in French), http://www.orange.com/fr/presse/communiques/communiques-2012/Orange-Money-depasse-les-4-millions-de-clients-et-lance-ses-services-en-Jordanie-et-a-l-Ile-Maurice
Prefuse Information Visualization toolkit, http://prefuse.org/
Rieke, R., Coppolino, L., Hutchison, A., Prieto, E., Gaber, C.: Security and Reliability Requirements for Advanced Security Event Management. In: Kotenko, I., Skormin, V. (eds.) MMM-ACNS 2012. LNCS, vol. 7531, pp. 171–180. Springer, Heidelberg (2012)
Ron, D., Shamir, A.: Quantitative Analysis of the Full Bitcoin Transaction Graph. In: Sadeghi, A.-R. (ed.) FC 2013. LNCS, vol. 7859, pp. 6–24. Springer, Heidelberg (2013)
SAS Fraud detection solutions, http://www.sas.com/offices/europe/uk/industries/banking/fraud-detection.html (viewed on the October 10, 2013)
Schreck, T., Tekusova, T., Kohlhammer, J., Fellner, D.: Trajectory-based visual analysis of large financial time series data. ACMSIGKDD Explorations Newsletter 9(2), 30–37 (2007)
Second quarter of the financial year 2012/2013. Quarterly sector statistics report. Communications Commission of Kenya (2012)
Shneiderman, B.: Dynamic queries for visual information seeking. In: The Craft of Information Visualization: Readings and Reflections, pp. 14–21. Morgan Kaufman (2003)
Wattenberg, M.: Visualizing the stock market. In: CHI Extended Abstracts on Human Factors in Computing Systems, pp. 188–189 (1999)
Westphal, C.R.: Patterns for Financial Intelligence Units (FIUs) and Anti-Money Laundering (AML) Operations, http://support.visualanalytics.com/technicalArticles/whitePaper/pdf/VAI%20AML%20FIU%20Patterns%20Presentation.pdf
Ziegler, H., Jenny, M., Gruse, T., Keim, D.A.: Visual Market Sector Analysis for Financial Time Series Data. In: IEEE Symposium on Visual Analytics Science and Technology (VAST), October 25-26, pp. 83–90 (2010)
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Novikova, E., Kotenko, I. (2014). Visual Analytics for Detecting Anomalous Activity in Mobile Money Transfer Services. In: Teufel, S., Min, T.A., You, I., Weippl, E. (eds) Availability, Reliability, and Security in Information Systems. CD-ARES 2014. Lecture Notes in Computer Science, vol 8708. Springer, Cham. https://doi.org/10.1007/978-3-319-10975-6_5
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DOI: https://doi.org/10.1007/978-3-319-10975-6_5
Publisher Name: Springer, Cham
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