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2020 | OriginalPaper | Buchkapitel

Unsupervised Machine Learning for Card Payment Fraud Detection

verfasst von : Mario Parreno-Centeno, Mohammed Aamir Ali, Yu Guan, Aad van Moorsel

Erschienen in: Risks and Security of Internet and Systems

Verlag: Springer International Publishing

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Abstract

Credit card fraud is one of the most common cybercrimes experienced by consumers today. Machine learning approaches are increasingly used to improve the accuracy of fraud detection systems. However, most of the approaches proposed so far have been based on supervised models, i.e., models trained with labelled historical fraudulent transactions, thus limiting the ability of the approach to recognise unknown fraud patterns. In this paper, we propose an unsupervised fraud detection system for card payments transactions. The unsupervised approach learns the characteristics of normal transactions and then identify anomalies as potential frauds. We introduce the challenges on modelling card payment transactions and discuss how to select the best features. Our approach can reduce the equal error rate (EER) significantly over previous approaches (from \(11.2\%\) to \(8.55\% ERR\)), for a real-world transaction dataset.

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Vorheriges Kapitel “I do it because they do it”: Social-Neutralisation in Information Security Practices of Saudi Medical Interns

Nächstes Kapitel Intrusion Detection Study and Enhancement Using Machine Learning

Aleskerov, E., et al.: CARDWATCH: a neural network based database mining system for credit card fraud detection. In: Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering, pp. 220–226 (1997)

Bansal, M.: Credit card fraud detection using self organised map. Int. J. Inf. Comput. Technol. 4, 1343–1348 (2014)

Bekirev, A.S., Klimov, V.V., Kuzin, M.V., Shchukin, B.A.: Payment card fraud detection using neural network committee and clustering. Opt. Mem. Neural Netw. 24(3), 193–200 (2015). https://doi.org/10.3103/S1060992X15030030CrossRef

Benson Edwin Raj, S., et al.: Analysis on credit card fraud detection methods. In: 2011 International Conference on Computer, Communication and Electrical Technology, pp. 152–156, March 2011

Bhattacharyya, S., Jha, S., Tharakunnel, K., Westland, J.C.: Data mining for credit card fraud: a comparative study. Decis. Support Syst. 50(3), 602–613 (2011). https://doi.org/10.1016/j.dss.2010.08.008, http://www.sciencedirect.com/science/article/pii/S0167923610001326, on quantitative methods for detection of financial fraud

Bhusari, V., Patil, S.: Application of hidden Markov model in credit card fraud detection. Int. J. Distrib. Parallel Syst. 2, 203 (2011)CrossRef

Brause, R., et al.: Neural data mining for credit card fraud detection. In: Proceedings 11th International Conference on Tools with Artificial Intelligence, pp. 103–106 (1999)

Capgemini; BNP Paribas: World payments report 2017. Technical report (2017)

Centeno, M.P., et al.: Smartphone continuous authentication using deep learning autoencoders. In: 2017 15th Annual Conference on Privacy, Security and Trust (PST), pp. 147–1478, August 2017. https://doi.org/10.1109/PST.2017.00026

10.

Centeno, M.P., et al.: Mobile based continuous authentication using deep features. In: Proceedings of the 2nd International Workshop on Embedded and Mobile Deep Learning, EMDL 2018, pp. 19–24 (2018)

11.

Deng, L., et al.: New types of deep neural network learning for speech recognition and related applications: an overview. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 8599–8603, May 2013

12.

Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 184–199. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10593-2_13CrossRef

13.

European Central Bank: Fifth report on card fraud. Technical report (2018)

14.

Fu, K., Cheng, D., Tu, Y., Zhang, L.: Credit card fraud detection using convolutional neural networks. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds.) ICONIP 2016. LNCS, vol. 9949, pp. 483–490. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46675-0_53CrossRef

15.

Gadi, M.F.A., Wang, X., do Lago, A.P.: Credit card fraud detection with artificial immune system. In: Bentley, P.J., Lee, D., Jung, S. (eds.) ICARIS 2008. LNCS, vol. 5132, pp. 119–131. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85072-4_11CrossRef

16.

Ghosh, S., Reilly, D.L.: Credit card fraud detection with a neural-network. In: 1994 Proceedings of Twenty-Seventh Hawaii International Conference on System Science, pp. 621–630 (1994). https://doi.org/10.1109/HICSS.1994.323314

17.

Hejazi, M., et al.: One-class support vector machines approach to anomaly detection. Appl. Artif. Intell. 27, 351–366 (2013)CrossRef

18.

Hinton, G., et al.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Sig. Process. Mag. 6, 82–97 (2012)CrossRef

19.

Iyer, D., et al.: Credit card fraud detection using Hidden Markov Model. In: 2011 World Congress on Information and Communication Technologies, pp. 1062–1066, December 2011

20.

Jurgovsky, J., et al.: Sequence classification for credit-card fraud detection. Exp. Syst. Appl. 100, 234–245 (2018). https://doi.org/10.1016/j.eswa.2018.01.037CrossRef

21.

Karlik, B., Olgac, A.: Performance analysis of various activation functions in generalized MLP architectures of neural networks. Int. J. Artif. Intell. Exp. Syst. (IJAE) 1(4), 111–122 (2010)

22.

KhanT, A., et al.: Credit card fraud detection using hidden Markov model 2, 1062–1066 (2011)

23.

Louppe, G.: Understanding random forests: from theory to practice. Ph.D. thesis, October 2014

24.

Maes, S., Tuyls, K., Vanschoenwinkel, B., Manderick, B.: Credit card fraud detection using Bayesian and neural networks. In: Maciunas, R.J. (ed.) Interactive Image-Guided Neurosurgery. American Association Neurological Surgeons, pp. 261–270 (1993)

25.

Pozzolo, A.D., Caelen, O., Johnson, R.A., Bontempi, G.: Calibrating probability with undersampling for unbalanced classification. In: Proceedings of - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015, pp. 159–166 (2015). https://doi.org/10.1109/SSCI.2015.33

26.

Quah, J.T., et al.: Real-time credit card fraud detection using computational intelligence. Exp. Syst. Appl. 35(4), 1721–1732 (2008)CrossRef

27.

Ryman-Tubb, N.F., Krause, P., Garn, W.: How artificial intelligence and machine learning research impacts payment card fraud detection: a survey and industry benchmark. Eng. Appl. Artif. Intell. 76, 130–157 (2018). https://doi.org/10.1016/j.engappai.2018.07.008, http://www.sciencedirect.com/science/article/pii/S0952197618301520

28.

Sahin, Y., Duman, E.: Detecting credit card fraud by decision trees and support vector machines. In: International Multi Conference of Engineers and Computer Scientists, IMECS 2011, vol. 1, pp. 442–447, March 2011

29.

Socher, R., et al.: Convolutional-recursive deep learning for 3D object classification. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, NIPS 2012, vol. 1, pp. 656–664 (2012)

30.

Srivastava, A., et al.: Credit card fraud detection using hidden Markov model. IEEE Trans. Dependable Secure Comput. 5, 37–38 (2008)CrossRef

31.

Tech, V.M.: Fraud detection in credit card by clustering approach. Int. J. Comput. Appl. 98(3), 975–8887 (2014)

32.

Bryan, T., et al.: Card-Not-Present Fraud around the World. U.S. Payments Forum 1, March 2017. https://www.uspaymentsforum.org/wp-content/uploads/2017/03/CNP-Fraud-Around-the-World-WP-FINAL-Mar-2017.pdf

33.

Ush, A., Khan, S., Akhtar, N., Qureshi, M.N.: Real-time credit-card fraud detection using artificial neural network tuned by simulated annealing algorithm

34.

Vatsa, V., Sural, S., Majumdar, A.K.: A game-theoretic approach to credit card fraud detection. In: Jajodia, S., Mazumdar, C. (eds.) ICISS 2005. LNCS, vol. 3803, pp. 263–276. Springer, Heidelberg (2005). https://doi.org/10.1007/11593980_20CrossRef

35.

Vincent, P., et al.: Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning, ICML 2008, pp. 1096–1103 (2008). https://doi.org/10.1145/1390156.1390294

36.

Wang, S.: A comprehensive survey of data mining-based accounting-fraud detection research. In: 2010 International Conference on Intelligent Computation Technology and Automation, vol. 1, pp. 50–53, May 2010. https://doi.org/10.1109/ICICTA.2010.831

37.

Whitrow, C., Hand, D.J., Juszczak, P., Weston, D., Adams, N.M.: Transaction aggregation as a strategy for credit card fraud detection. Data Min. Knowl. Discov. 18(1), 30–55 (2009). https://doi.org/10.1007/s10618-008-0116-zMathSciNetCrossRef

38.

WorldPay: The art and science of global payments a definitive report from Worldpay Global Payments Report, November 2018

39.

Yaginuma, Y., et al.: Multi-sensor fusion model for constructing internal representation using autoencoder neural networks. In: Proceedings of International Conference on Neural Networks (ICNN 1996), vol. 3, pp. 1646–1651, June 1996

Titel: Unsupervised Machine Learning for Card Payment Fraud Detection
verfasst von: Mario Parreno-Centeno
Mohammed Aamir Ali
Yu Guan
Aad van Moorsel
Verlag: Springer International Publishing
Buch: Risks and Security of Internet and Systems
Print ISBN: 978-3-030-41567-9

Electronic ISBN: 978-3-030-41568-6

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-41568-6_16

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