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Application of cluster analysis for the assessment of the share of fraud victims among bank card holders

Published: 08 September 2015 Publication History

Abstract

In this paper, we present a method for the assessment of the share of cardholders most prone to various types of bank fraud (i.e. fishing, vishing, skimming). For this purpose, a forecasting information system has been designed. It is based on a clustering module used for output of a certain set of cluster indices that depend on the percentage of aggrieved clients in the training sample. The k-means method is used for clustering. The initial coordinates of centroids are defined using advanced k-means++ algorithm.

References

[1]
Galyamov, A. F. and Tarkhov, S. V. 2014. Upravlenie vzaimodeystviem s klientami kommercheskoy organizatsii na osnove metodov segmentatsii i klasterizatsii klientskoy bazy. Vestnik UGATU. 18, 4 (65), 149--156.
[2]
Alkhasov, S. S. and Tselykh, A. N. 2014. Printsipy postroeniya prognosticheskoy sistemy dlya modelirovaniya ottoka klientov uslug Interneta. In Proceedings of the All-Russian Conference on Systems and Models in Information Epoch. Part 1. Southern Federal University (Russia), Taganrog, 4--6.
[3]
Alkhasov, S. S. and Tselykh, A. N. 2015. Osnovnye podkhody k postroeniyu informatcionnoy sistemy dlya modelirovaniya ottoka klientov uslug svyazi. Izvestiya SFedU. Engineering Sciences. 163, 2.
[4]
Efimov, A. 2013. Uderzhanie klienta -- ne iskusstvo, a prikladnaya nauka. URL=http://library.croc.ru/download/11342/930706a746182066f55bfe8f5800b735.pdf.
[5]
Sergeev, N. E., Tselykh, A. A. and Tselykh, A. N. 2013. Generalized Approach to Modeling User Activity Graphs for Network Security and Public Safety Monitoring. In Proceedings of the 6th International Conference on Security of Information and Networks. 117--122. DOI=http://dx.doi.org/10.1145/2523514.2523534.
[6]
Gakhov, A. V. 2014. Data Mining. Lecture 4. Preprocessing. Part 3. URL=http://www.slideshare.net/gakhov/4-39539775.
[7]
Jain, A., Nandakumar, K. and Ross, A. 2005. Score normalization in multimodal biometric systems. Pattern Recognition. 38, 2270--2285. DOI=http://dx.doi.org/10.1016/j.patcog.2005.01.012.
[8]
Keramati, A., Jafari-Marandi, R., Aliannejadi, M., Ahmadian, I., Mozaffari, M. and Abbasi, U. 2014. Improved churn prediction in telecommunication industry using data mining techniques. Applied Soft Computing. 24, 994--1012. DOI= http://dx.doi.org/10.1016/j.asoc.2014.08.041.
[9]
Berikov, V. B. and Lbov G. S. 2008. Sovremennye tendentsii v klasternom analize. All-Russian competitive selection of survey and analytical articles on priority area "Information telecommunication systems". URL=http://window.edu.ru/resource/161/56161.
[10]
Lepskiy, A. E. and Bronevich, A. G. 2009. Matematicheskie metody raspoznavaniya obrazov. Southern Federal University (Russia), Taganrog. URL=http://lepskiy.ucoz.com/lect_Lepskiy_Bronevich_pass.pdf.
[11]
Tu, J. and Gonsales, R. 1978. Printcipy raspoznavaniya obrazov. Mir Publishing, Moscow.
[12]
Barsegyan, A. A., Cupriyanov, M. S., Stepanenko, V. V. and Kholod, I. I. 2008. Tekhnologii analiza dannykh: Data Mining, Visual Mining, Text Mining, OLAP. BHV-Peterburg Publishing, Saint Petersburg.
[13]
Arthur, D. and Vassilvitskii, S. 2007. k-means++: the advantages of careful seeding. In Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2007). 1027--1035.
[14]
Bahmani, B., Moseley, B., Vattani, A., Kumar, R. and Vassilvitskii, S. 2012. Scalable k-means++. In Proceedings of the VLDB Endowment. 5, 7, 622--633. DOI=http://dx.doi.org/10.14778/2180912.2180915.
[15]
Meleshin, I. 2013. Algoritmy mashinnogo obucheniya i analiza dannykh v MATLAB. URL=http://www.mathworks.com/videos/machine-learning-in-matlab-99098.html.
[16]
Zimichev, E. A., Kazanskiy, N. L. and Serafimovich, P. G. 2014. Prostranstvennaya klassifikatsiya giperspektralnykh izobrazheniy s ispolzovaniem metoda klasterizatsii k-means++. Kompyuternaya optika. 38, 2, 281--286. URL= http://www.computeroptics.smr.ru/KO/PDF/KO38-2/380217.pdf.
[17]
Dyakonov, A. G. 2010. Analiz dannykh, obuchenie po pretsedentam, logicheskie igry, sistemy WEKA, RapidMiner i MATLAB. Moscow State University.
[18]
Abernethy, M. 2010. Data mining with WEKA, Part 1: Introduction and regression. URL=https://www.ibm.com/developerworks/library/os-weka1/.
[19]
Bozhenyuk, A. V., Kotov, E. M. and Tselykh, A. A. 2009. Intellektualnye internet-tekhnologii. Phoenix Publishing, Rostov-on-Don.

Cited By

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  • (2024)Clustering on hierarchical heterogeneous data with prior pairwise relationshipsBMC Bioinformatics10.1186/s12859-024-05652-625:1Online publication date: 23-Jan-2024
  • (2024)An innovative clustering approach utilizing frequent item setsMultimedia Tools and Applications10.1007/s11042-024-18913-6Online publication date: 26-Apr-2024
  • (2020)An Improved XGBoost Model Based on Spark for Credit Card Fraud Prediction2020 IEEE 5th International Symposium on Smart and Wireless Systems within the Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS)10.1109/IDAACS-SWS50031.2020.9297058(1-6)Online publication date: 17-Sep-2020
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Published In

cover image ACM Other conferences
SIN '15: Proceedings of the 8th International Conference on Security of Information and Networks
September 2015
350 pages
ISBN:9781450334532
DOI:10.1145/2799979
Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 September 2015

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Author Tags

  1. banking
  2. clustering
  3. credit card
  4. fraud
  5. k-means
  6. k-means++

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SIN '15

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SIN '15 Paper Acceptance Rate 34 of 92 submissions, 37%;
Overall Acceptance Rate 102 of 289 submissions, 35%

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Cited By

View all
  • (2024)Clustering on hierarchical heterogeneous data with prior pairwise relationshipsBMC Bioinformatics10.1186/s12859-024-05652-625:1Online publication date: 23-Jan-2024
  • (2024)An innovative clustering approach utilizing frequent item setsMultimedia Tools and Applications10.1007/s11042-024-18913-6Online publication date: 26-Apr-2024
  • (2020)An Improved XGBoost Model Based on Spark for Credit Card Fraud Prediction2020 IEEE 5th International Symposium on Smart and Wireless Systems within the Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS)10.1109/IDAACS-SWS50031.2020.9297058(1-6)Online publication date: 17-Sep-2020
  • (2019)A Short Review on Different Clustering Techniques and Their ApplicationsEmerging Technology in Modelling and Graphics10.1007/978-981-13-7403-6_9(69-83)Online publication date: 17-Jul-2019

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