Abstract
The exponential growth in the number of operations carried out in the e-commerce environment is directly related to the growth in the number of operations performed through credit cards. This happens because practically all commercial operators allow their customers to make their payments by using them. Such scenario leads toward an high level of risk related to the potential fraudulent activities that the fraudsters can perform by exploiting this powerful instrument of payment illegitimately. A large number of state-of-the-art approaches have been designed to address this problem, but they must face some common issues, the most important of them are the imbalanced distribution and the heterogeneity of data. This paper presents a novel fraud detection approach based on the Discrete Wavelet Transform, which is exploited in order to define an evaluation model able to address the aforementioned problems. Such objective is achieved by using only legitimate transactions in the model definition process, an operation made possible by the more stable data representation offered by the new domain. The performed experiments show that our approach performance is comparable to that of one of the best state-of-the-art approaches such as random forests, demonstrating how such proactive strategy is also able to face the cold-start problem.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chaovalit, P., Gangopadhyay, A., Karabatis, G., Chen, Z.: Discrete wavelet transform-based time series analysis and mining. ACM Comput. Surv. 43(2), 6:1–6:37 (2011)
Bolton, R.J., Hand, D.J.: Statistical fraud detection: a review. Stat. Sci. 17, 235–249 (2002)
Pozzolo, A.D., Caelen, O., Borgne, Y.L., Waterschoot, S., Bontempi, G.: Learned lessons in credit card fraud detection from a practitioner perspective. Expert Syst. Appl. 41(10), 4915–4928 (2014)
Wang, H., Fan, W., Yu, P.S., Han, J.: Mining concept-drifting data streams using ensemble classifiers. In: Getoor, L., Senator, T.E., Domingos, P.M., Faloutsos, C. (eds.) Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, pp. 226–235. ACM, 24–27 August 2003
Gao, J., Fan, W., Han, J., Yu, P.S.: A general framework for mining concept-drifting data streams with skewed distributions. In: Proceedings of the Seventh SIAM International Conference on Data Mining, Minneapolis, Minnesota, USA, pp. 3–14. SIAM, 26–28 April 2007
Phua, C., Lee, V., Smith, K., Gayler, R.: A comprehensive survey of data mining-based fraud detection research (2010)
Sorournejad, S., Zojaji, Z., Atani, R.E., Monadjemi, A.H.: A survey of credit card fraud detection techniques: data and technique oriented perspective. CoRR abs/1611.06439 (2016)
Chatterjee, A., Segev, A.: Data manipulation in heterogeneous databases. ACM SIGMOD Rec. 20(4), 64–68 (1991)
Japkowicz, N., Stephen, S.: The class imbalance problem: a systematic study. Intell. Data Anal. 6(5), 429–449 (2002)
Donmez, P., Carbonell, J.G., Bennett, P.N.: Dual strategy active learning. In: Kok, J.N., Koronacki, J., Mantaras, R.L., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS, vol. 4701, pp. 116–127. Springer, Heidelberg (2007). doi:10.1007/978-3-540-74958-5_14
Chernick, M.R.: Wavelet methods for time series analysis. Technometrics 43(4), 491 (2001)
Percival, D.B., Walden, A.T.: Wavelet Methods for Time Series Analysis, vol. 4. Cambridge University Press, Cambridge (2006)
Mallat, S.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Lessmann, S., Baesens, B., Seow, H., Thomas, L.C.: Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research. Eur. J. Oper. Res. 247(1), 124–136 (2015)
Brown, I., Mues, C.: An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Syst. Appl. 39(3), 3446–3453 (2012)
Dal Pozzolo, A., Caelen, O., Johnson, R.A., Bontempi, G.: Calibrating probability with undersampling for unbalanced classification. In: 2015 IEEE Symposium Series on Computational Intelligence, pp. 159–166. IEEE (2015)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
Acknowledgments
This research is partially funded by Regione Sardegna under project Next generation Open Mobile Apps Development (NOMAD), Pacchetti Integrati di Agevolazione (PIA) Industria Artigianato e Servizi (2013).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Saia, R. (2017). A Discrete Wavelet Transform Approach to Fraud Detection. In: Yan, Z., Molva, R., Mazurczyk, W., Kantola, R. (eds) Network and System Security. NSS 2017. Lecture Notes in Computer Science(), vol 10394. Springer, Cham. https://doi.org/10.1007/978-3-319-64701-2_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-64701-2_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-64700-5
Online ISBN: 978-3-319-64701-2
eBook Packages: Computer ScienceComputer Science (R0)