AUTHORS: Igor Mekterović, Ljiljana Brkić, Mirta Baranović

Download as PDF

ABSTRACT: Credit card fraud is a serious and ever-growing problem with billions of dollars lost every year due to fraudulent transactions. Fraud has always been present and will always be. It is also ever changing, as the technology and usage patterns change over time, which makes CCFD (credit card fraud detection) a particularly hard problem. Traditionally, fraud detection relied solely on domain experts’ detection rules, but in the past decade or two, such solutions are being augmented with data mining models for fraud detection. The progress in this area is impeded both by the sensitive nature of the data and great commercial potential – the industrial solutions are understandably kept secret and authentic datasets are rare and few. In this paper we study the CCFD problem with its typical problems and state of the art solution. We survey the recent literature and bring a structured overview of relevant fraud detection features and data mining approaches to this problem.

KEYWORDS: - credit card, fraud detection, machine learning, systematic review

REFERENCES:

[1] A. Orendorff, “No Title,” Global Ecommerce: Statistics and International Growth Trends.

[Online]. Available: https://www.shopify.com/enterprise/global-ecommercestatistics.

[2] R. Aitken, “No Title,” U.S. Card Fraud Losses Could Exceed 12B USD By 2020, 2016.

[Online]. Available: http://www.forbes.com/sites/rogeraitken/2016/10/26/us-cardfraud-losses-could-exceed-12bn-by-2020/.

[3] A. of C. F. Examiners, “No Title.”

[Online]. Available: http://www.acfe.com/rttn-introduction.aspx.

[4] R. J. Bolton, D. J. Hand, and D. J. H, “Unsupervised Profiling Methods for Fraud Detection,” Proc. Credit Scoring Credit Control VII, pp. 5–7, 2001.

[5] E. C. Bank, “Fourth report on card fraud,” 2015.

[6] D. J. Hand and G. Blunt, “Prospecting for gems in credit card data,” IMA J. Manag. Math., vol. 12, no. 2, pp. 173–200, 2001.

[7] V. Hanagandi, A. Dhar, and K. Buescher, “Density-based clustering and radial basis function modeling to generate credit card fraud scores,” in Computational Intelligence for Financial Engineering, 1996., Proceedings of the IEEE/IAFE 1996 Conference on, 1996, pp. 247–251.

[8] T. Milo and W. Tan, “Interactive Rule Refinement for Fraud Detection.”

[9] J. R. Dorronsoro, F. Ginel, C. Sánchez, and C. Santa Cruz, “Neural fraud detection in credit card operations,” IEEE Trans. Neural Networks, vol. 8, no. 4, pp. 827–834, 1997.

[10] Ghosh and Reilly, “Credit card fraud detection with a neuralnetwork,” 1994 Proc. Twenty-Seventh Hawaii Int. Conf. Syst. Sci., vol. 3, pp. 621–630, 1994.

[11] J. A. Gómez, J. Arévalo, R. Paredes, and J. Nin, “End-to-end neural network architecture for fraud scoring in card payments,” Pattern Recognit. Lett., vol. 105, pp. 175–181, 2018.

[12] C. Whitrow, D. J. Hand, P. Juszczak, D. Weston, and N. M. Adams, “Transaction aggregation as a strategy for credit card fraud detection,” Data Min. Knowl. Discov., vol. 18, no. 1, pp. 30–55, 2009.

[13] S. Bhattacharyya, S. Jha, K. Tharakunnel, and J. C. Westland, “Data mining for credit card fraud: A comparative study,” Decis. Support Syst., vol. 50, no. 3, pp. 602–613, 2011.

[14] P. Ravisankar, V. Ravi, G. Raghava Rao, and I. Bose, “Detection of financial statement fraud and feature selection using data mining techniques,” Decis. Support Syst., vol. 50, no. 2, pp. 491–500, 2011.

[15] S. Jha, M. Guillen, and J. Christopher Westland, “Employing transaction aggregation strategy to detect credit card fraud,” Expert Syst. Appl., vol. 39, no. 16, pp. 12650–12657, 2012.

[16] A. Correa Bahnsen, D. Aouada, A. Stojanovic, and B. Ottersten, “Feature engineering strategies for credit card fraud detection,” Expert Syst. Appl., vol. 51, pp. 134–142, 2016.

[17] K. Randhawa, C. K. Loo, M. Seera, C. P. Lim, and A. K. Nandi, “Credit Card Fraud Detection Using AdaBoost and Majority Voting,” IEEE Access, vol. 6, pp. 14277–14284, 2018.

[18] F. Carcillo, A. Dal Pozzolo, Y. A. Le Borgne, O. Caelen, Y. Mazzer, and G. Bontempi, “SCARFF: A scalable framework for streaming credit card fraud detection with spark,” Inf. Fusion, vol. 41, pp. 182–194, 2018.

[19] J. Jurgovsky et al., “Sequence classification for credit-card fraud detection,” Expert Syst. Appl., vol. 100, pp. 234–245, 2018.

[20] J. Xu, A. H. Sung, and Q. Liu, “Behaviour mining for fraud detection,” J. Res. Pract. Inf. Technol., vol. 39, no. 1, pp. 3– 18, 2007.

[21] D. Sánchez, M. A. Vila, L. Cerda, and J. M. Serrano, “Association rules applied to credit card fraud detection,” Expert Syst. Appl., vol. 36, no. 2 PART 2, pp. 3630–3640, 2009.

[22] A. Artikis et al., “Industry paper: A prototype for credit card fraud management,” DEBS 2017 - Proc. 11th ACM Int. Conf. Distrib. Event-Based Syst., 2017.

[23] L. W. Vona, Fraud Data Analytics Methodology: The Fraud Scenario Approach to Uncovering Fraud in Core Business Systems. John Wiley & Sons, 2017.

[24] W. V. Bart Baesens, Veronique Van Vlasselaer, Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection. John Wiley & Sons, 2015.

[25] M. L. G.- ULB, “Credit Card Fraud Detection Anonymized credit card transactions labeled as fraudulent or genuine.”

[Online]. Available: https://www.kaggle.com/mlgulb/creditcardfraud.

[26] A. Dal Pozzolo, “Adaptive Machine Learning for Credit Card Fraud Detection Declaration of Authorship,” no. December, p. 199, 2015.

[27] S. Project, “No Title.”

[Online]. Available: http://speeddproject.eu/data.

[28] G. E. A. P. A. Batista, A. C. P. L. F. Carvalho, and M. C. Monard, “Applying one-sided selection to unbalanced datasets,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 1793 LNAI, pp. 315–325, 2000.

[29] N. Chawla and K. Bowyer, “SMOTE: Synthetic Minority Over-sampling Technique Nitesh,” J. Artif. Intell. Res., vol. 16, pp. 321–357, 2002.

[30] Y. Sahin, S. Bulkan, and E. Duman, “A cost-sensitive decision tree approach for fraud detection,” Expert Syst. Appl., vol. 40, no. 15, pp. 5916–5923, 2013.

[31] X. Y. Liu, J. Wu, and Z. H. Zhou, “Exploratory undersampling for class-imbalance learning,” Proc. - IEEE Int. Conf. Data Mining, ICDM, pp. 965–969, 2006.

[32] T. R. Hoens, R. Polikar, and N. V. Chawla, “Learning from streaming data with concept drift and imbalance: an overview,” Prog. Artif. Intell., vol. 1, no. 1, pp. 89–101, 2012.

[33] A. Dal Pozzolo, O. Caelen, Y. A. Le Borgne, S. Waterschoot, and G. Bontempi, “Learned lessons in credit card fraud detection from a practitioner perspective,” Expert Syst. Appl., vol. 41, no. 10, pp. 4915–4928, 2014.

[34] T. R. Hoens, R. Polikar, and N. V. Chawla, “Learning from streaming data with concept drift and imbalance: an overview,” Prog. Artif. Intell., vol. 1, no. 1, pp. 89–101, 2012.

[35] S. Panigrahi, A. Kundu, S. Sural, and A. K. Majumdar, “Credit card fraud detection: A fusion approach using Dempster-Shafer theory and Bayesian learning,” Inf. Fusion, vol. 10, no. 4, pp. 354–363, 2009.

[36] B. Wiese and C. Omlin, “Credit card transactions, fraud detection, and machine learning: Modelling time with LSTM recurrent neural networks,” Stud. Comput. Intell., vol. 247, pp. 231–268, 2009.

[37] N. F. Ryman-Tubb and P. Krause, “Neural network rule extraction to detect credit card fraud,” IFIP Adv. Inf. Commun. Technol., vol. 363 AICT, no. PART 1, pp. 101– 110, 2011.

[38] N. Wong, P. Ray, G. Stephens, and L. Lewis, “Artificial immune systems for the detection of credit card fraud: An architecture, prototype and preliminary results,” Inf. Syst. J., vol. 22, no. 1, pp. 53–76, 2012.

[39] M. Hejazi and Y. P. Singh, “One-class support vector machines approach to anomaly detection,” Appl. Artif. Intell., vol. 27, no. 5, pp. 351–366, 2013.

[40] A. C. Bahnsen, A. Stojanovic, D. Aouada, and B. Ottersten, “Cost sensitive credit card fraud detection using bayes minimum risk,” Proc. - 2013 12th Int. Conf. Mach. Learn. Appl. ICMLA 2013, vol. 1, pp. 333–338, 2013.

[41] E. Duman and I. Elikucuk, “Solving Credit Card Fraud Detection Problem by the New Metaheuristics Migrating Birds Optimization,” pp. 62–71, 2013.

[42] D. Olszewski, “Fraud detection using self-organizing map visualizing the user profiles,” Knowledge-Based Syst., vol. 70, pp. 324–334, 2014.

[43] N. Soltani Halvaiee and M. K. Akbari, “A novel model for credit card fraud detection using Artificial Immune Systems,” Appl. Soft Comput. J., vol. 24, pp. 40–49, 2014.

[44] V. Van Vlasselaer et al., “APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions,” Decis. Support Syst., vol. 75, pp. 38–48, 2015.

[45] A. C. Bahnsen, D. Aouada, and B. Ottersten, “Exampledependent cost-sensitive decision trees,” Expert Syst. Appl., vol. 42, no. 19, pp. 6609–6619, 2015.

[46] N. Mahmoudi and E. Duman, “Detecting credit card fraud by Modified Fisher Discriminant Analysis,” Expert Syst. Appl., vol. 42, no. 5, pp. 2510–2516, 2015.

[47] A. Dal Pozzolo, G. Boracchi, O. Caelen, C. Alippi, and G. Bontempi, “Credit card fraud detection and concept-drift adaptation with delayed supervised information,” Proc. Int. Jt. Conf. Neural Networks, vol. 2015–Septe, 2015.

[48] F. Ghobadi and M. Rohani, “Cost sensitive modeling of credit card fraud using neural network strategy,” Proc. - 2016 2nd Int. Conf. Signal Process. Intell. Syst. ICSPIS 2016, pp. 8–10, 2017.

[49] T. Liu and S. Liu, “Fraud detection model & application for credit card acquiring business based on data mining technology,” vol. 50, no. Iceeecs, pp. 963–967, 2016.

[50] A. Zakaryazad and E. Duman, “A profit-driven Artificial Neural Network (ANN) with applications to fraud detection and direct marketing,” Neurocomputing, vol. 175, no. PartA, pp. 121–131, 2016.

[51] N. Carneiro, G. Figueira, and M. Costa, “A data mining based system for credit-card fraud detection in e-tail,” Decis. Support Syst., vol. 95, pp. 91–101, 2017.

[52] Y. Kültür and M. U. Çağlayan, “Hybrid approaches for detecting credit card fraud,” Expert Syst., vol. 34, no. 2, pp. 1–13, 2017.

[53] W. N. Robinson and A. Aria, “Sequential fraud detection for prepaid cards using hidden Markov model divergence,” Expert Syst. Appl., vol. 91, pp. 235–251, 2018.

WSEAS Transactions on Business and Economics, ISSN / E-ISSN: 1109-9526 / 2224-2899, Volume 15, 2018, Art. #43, pp. 437-444


Copyright Β© 2018 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0