Document Type : Original/Review Paper
Authors
Department of Computer Engineering, Alzahra University, Tehran, Iran.
Abstract
With the advancement of technology, the daily use of bank credit cards has been increasing exponentially. Therefore, the fraudulent use of credit cards by others as one of the new crimes is also growing fast. For this reason, detecting and preventing these attacks has become an active area of study. This article discusses the challenges of detecting fraudulent banking transactions and presents solutions based on deep learning. Transactions are examined and compared with other traditional models in fraud detection. According to the results obtained, optimal performance is related to the combined model of deep convolutional networks and short-term memory, which is trained using the aggregated data received from the generative adversarial network. This paper intends to produce sensible data to address the unequal class distribution problem, which is far more effective than traditional methods. Also, it uses the strengths of the two approaches by combining deep convolutional network and Long Short Term Memory network to improve performance. Due to the inefficiency of evaluation criteria such as accuracy in this application, the measure of distance score and the equal error rate has been used to evaluate models more transparent and more precise. Traditional methods were compared to the proposed approach to evaluate the efficiency of the experiment.
Keywords
[1] 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.
[2] Nilson Report (2019), The Nilson Report, Issue 1164, November. Retrieved from "https://nilsonreport.com/upload/content_promo/The_Nilson_Report_Issue_1164.pdf"
[3] T. Eliassi-Rad et al., “APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions,” Decis. Support Syst., Vol. 75, No. 2015, pp. 38–48, 2015.
[4] X. S. E.W.T. Ngai, H. Yong., Y.H. Wong, and Y. Chen, “The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature,” Decis. Support Syst., Vol. 50, pp. 559–569, 2011.
[5] J. C. W. Jha.Sanjeev and G. Montserrat, “Employing transaction aggregation strategy to detect credit card fraud,” Expert Syst. with Appl., Vol. 39, pp. 12650–12657, 2012.
[6] J. D. J. Piotr., A.M. Niall, J.D. Hand, and C. Whitrow, “Off the peg and bespoke classifiers for fraud detection,” Comput. Stat. Data Anal., Vol. 52, pp. 4521–4532, 2008.
[7] S. Kumar, V. Kumar-Solanki, S. K. Choudhary, A. Selamat, and R. Gonzalez-Crespo, “Comparative Study on Ant Colony Optimization (ACO) and K-Means Clustering Approaches for Jobs Scheduling and Energy Optimization Model in Internet of Things (IoT),” Int. J. Interact. Multimed. Artif. Intell., Vol. 6, No. 1, p. 107, 2020.
[8] Haibo He and E. A. Garcia, “Learning from Imbalanced Data,” IEEE Trans. Knowl. Data Eng., Vol. 21, No. 9, pp. 1263–1284, Sep. 2009.
[9] S. J. S. P.K. Chan, W. Fan, and A.L. Prodromidis, “Distributed data mining in credit card fraud detection,” Proc. IEEE Intell. Syst., pp. 67–74, 1999.
[10] D. J. H. R.J. Bolton, “Unsupervised profiling methods for fraud detection,” Conf. Credit scoring Credit Control ,Edinburgh, 2001.
[11] A. Eshghi and M. Kargari, “Introducing a new method for the fusion of fraud evidence in banking transactions with regards to uncertainty,” Expert Syst. Appl., Vol. 121, pp. 382–392, May 2019.
[12] U. Fiore, A. De Santis, F. Perla, P. Zanetti, and F. Palmieri, “Using generative adversarial networks for improving classification effectiveness in credit card fraud detection,” Inf. Sci. (NY)., Vol. 479, pp. 448–455, Apr. 2019.
[13] I. J. Goodfellow et al., “Generative adversarial nets,” in Advances in Neural Information Processing Systems, 2014,Vol. 3, No. January, pp. 2672–2680.
[14] D. Devarriya, C. Gulati, V. Mansharamani, A. Sakalle, and A. Bhardwaj, “Unbalanced breast cancer data classification using novel fitness functions in genetic programming,” Expert Syst. Appl., Vol. 140, Feb. 2020.
[15] Y. Heryadi and H. L. H. S. Warnars, “Learning temporal representation of transaction amount for fraudulent transaction recognition using CNN, Stacked LSTM, and CNN-LSTM,” in 2017 IEEE International Conference on Cybernetics and Computational Intelligence, CyberneticsCOM 2017-Proceedings, 2018, Vol. 2017-November, pp. 84–89.
[16] A. Ullah, J. Ahmad, K. Muhammad, M. Sajjad, and S. W. Baik, “Action Recognition in Video Sequences using Deep Bi-Directional LSTM with CNN Features,” IEEE Access, Vol. 6, pp. 1155–1166, Nov. 2017.
[17] S. L. Oh, E. Y. K. Ng, R. S. Tan, and U. R. Acharya, “Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heartbeats,” Comput. Biol. Med., Vol. 102, pp. 278–287, Nov. 2018.
[18] R. Zhao, R. Yan, J. Wang, and K. Mao, “Learning to monitor machine health with convolutional Bi-directional LSTM networks,” Sensors (Switzerland), Vol. 17, No. 2, Feb. 2017.
[19] M. Syeda, Y. Q. Zhang, and Y. Pan, “Parallel granular neural networks for fast credit card fraud detection,” in IEEE International Conference on Fuzzy Systems, 2002, Vol. 1, pp. 572–577.
[20] 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, Mar. 2017.
[21] 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, Feb. 2011.
[22] M. Carminati, R. Caron, F. Maggi, I. Epifani, and S. Zanero, “BankSealer: A decision support system for online banking fraud analysis and investigation,” Comput. Secur., vol. 53, pp. 175–186, Sep. 2015.
[23] 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, Jun. 2016.
[24] M. F. A. Gadi, X. Wang, and A. P. Do Lago, “Credit card fraud detection with artificial immune system,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008, Vol. 5132 LNCS, pp. 119–131.
[25] 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, pp. 4915–4928, 2014.
[26] 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, Feb. 2009.
[27] 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.
[28] S. Maes, S. Maes, K. Tuyls, B. Vanschoenwinkel, and B. Manderick, “Credit Card Fraud Detection Using Bayesian and Neural Networks,” MACIUNAS RJ, Ed. Interact. IMAGE-GUIDED NEUROSURGERY. Am. Assoc. Neurol. Surg., pp. 261--270, 1993.
[29] R. C. Chen, M. L. Chiu, Y. L. Huang, and L. T. Chen, “Detecting credit card fraud by using questionnaire-responded transaction model based on support vector machines,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), Vol. 3177, pp. 800–806, 2004.
[30] R. C. Chen, S. T. Luo, X. Liang, and V. C. S. Lee, “Personalized approach based on SVM and ANN for detecting credit card fraud,” in Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB’05, 2005, Vol. 2, pp. 810–815.
[31] P. K. Chan, W. Fan, A. L. Prodromidis, and S. J. Stolfo, “Distributed Data Mining in Credit Card Fraud Detection,” IEEE Intell. Syst. Their Appl., Vol. 14, No. 6, pp. 67–74, 1999.
[32] R. Brause, T. Langsdorf, and M. Hepp, “Neural data mining for credit card fraud detection,” in Proceedings of the International Conference on Tools with Artificial Intelligence, 1999, pp. 103–106.
[33] C. C. Chiu and C. Y. Tsai, “A web services-based collaborative scheme for credit card fraud detection,” in Proceedings - 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service, EEE 2004, 2004, pp. 177–181.
[34] X. Zhang, Y. Han, W. Xu, and Q. Wang, “HOBA: A novel feature engineering methodology for credit card fraud detection with a deep learning architecture,” Inf. Sci. (NY)., 2019.
[35] W. Lee, S. J. Stolfo, and K. W. Mok, “Adaptive intrusion detection: A data mining approach,” Artif. Intell. Rev., Vol. 14, No. 6, pp. 533–567, Dec. 2000.
[36] C. S. Hilas, “Designing an expert system for fraud detection in private telecommunications networks,” Expert Syst. Appl., Vol. 36, No. 9, pp. 11559–11569, Nov. 2009.
[37] A. Kanavos, S. A. Iakovou, S. Sioutas, and V. Tampakas, “Large scale product recommendation of supermarket ware based on customer behaviour analysis,” Big Data Cogn. Comput., Vol. 2, No. 2, pp. 1–19, Jun. 2018.
[38] R. A. Becker, C. Volinsky, and A. R. Wilks, “Fraud detection in telecommunications: History and lessons learned,” Technometrics, Vol. 52, No. 1, pp. 20–33, Feb. 2010.
[39] A. Sudjianto, S. Nair, M. Yuan, A. Zhang, D. Kern, and F. Cela-Díaz, “Statistical methods for fighting financial crimes,” Technometrics, Vol. 52, No. 1, pp. 5–19, Feb. 2010.
[40] D. J. Hand, “Fraud detection in telecommunications and banking: Discussion of Becker, Volinsky, and Wilks (2010) and Sudjianto et al. (2010),” Technometrics, Vol. 52, No. 1, pp. 34–38, Feb. 2010.
[41] G. Widmer, “Learning in the presence of concept drift and hidden contexts,” Mach. Learn., Vol. 23, No. 1, pp. 69–101, 1996.
[42] J. G. Moreno-Torres, T. Raeder, R. Alaiz-Rodríguez, N. V. Chawla, and F. Herrera, “A unifying view on dataset shift in classification,” Pattern Recognit., Vol. 45, No. 1, pp. 521–530, 2012.
[43] A. Tsymbal, “The problem of concept drift: definitions and related work,” 2004.
[44] J. Gama, I. Zliobaite, A. Bifet, M. Pechenizkiy, and A. Bouchachia, “A survey on concept drift adaptation,” ACM Computing Surveys, Vol. 46, No. 4. Association for Computing Machinery, 2014.
[45] S. Haykin and L. Li, “Nonlinear Adaptive Prediction of Nonstationary Signals,” IEEE Trans. Signal Process., Vol. 43, No. 2, pp. 526–535, 1995.
[46] R. Akbani, S. Kwek, and N. Japkowicz, “Applying support vector machines to imbalanced datasets,” in Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 2004, Vol. 3201, pp. 39–50.
[47] E. Aleskerov, B. Freisleben, B. Rao, Cardwatch: A neural network-based database mining system for credit card fraud detection, in: Proceedings of the IEEE/IAFE Computational Intelligence for Financial Engineering (CIFEr), IEEE, 1997, pp. 220-226
[48] T. Kim and S. Cho, "Predicting residential energy consumption using CNN-LSTM neural networks", Energy, Vol. 182, pp. 72-81, 2019. Available: 10.1016/j.energy.2019.05.230
[49] A. D. Pozzolo, O. Caelen, R. A. Johnson, and G. Bontempi, “Calibrating probability with undersampling for unbalanced classification,” in Proceedings-2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015, 2015, pp. 159–166.
[50] C. Bunkhumpornpat, K. Sinapiromsaran, and C. Lursinsap, “Safe-level-SMOTE: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009, Vol. 5476 LNAI, pp. 475–482.
[51] T. Razooqi, K. Raahemifar, P. Khurana, and A. Abhari, “Credit Card Fraud Detection Using Fuzzy Logic and Neural Network,” 2016.
[52] D. Lin, C. K. M. Lee, M. K. Siu, H. Lau, and K. L. Choy, “Analysis of customers’ return behavior after online shopping in China using SEM,” Ind. Manag. Data Syst., Vol. 120, No. 5, pp. 883–902, 2020.
[53] Z. Nematzadeh, R. Ibrahim, and A. Selamat, “Improving class noise detection and classification performance: A new two-filter CNDC model,” Appl. Soft Comput., Vol. 94, p. 106428, Sep. 2020.
[54] D. Kalaivani and T. Arunkumar, “Multi- process prediction model for customer behaviour analysis,” Int. J. Web Based Communities, Vol. 14, No. 1, pp. 54–63, 2018.
[55] S. Priya and R. A. Uthra, “Comprehensive analysis for class imbalance data with concept drift using ensemble-based classification,” J. Ambient Intell. Humaniz. Comput. 2020 125, Vol. 12, No. 5, pp. 4943–4956, Apr. 2020.
[56] M. Mohamad, A. Selamat, O. Krejcar, H. Fujita, and T. Wu, “An analysis on new hybrid parameter selection model performance over big data set,” Knowledge-Based Syst., Vol. 192, p. 105441, Mar. 2020.
[57] H. Mehmood, P. Kostakos, M. Cortes, T. Anagnostopoulos, S. Pirttikangas, and E. Gilman, “Concept Drift Adaptation Techniques in Distributed Environment for Real-World Data Streams,” Smart Cities, Vol. 4, No. 1, pp. 349–371, Mar. 2021.
[58] B. Lebichot, G. M. Paldino, G. Bontempi, W. Siblini, L. He-Guelton, and F. Oble, “Incremental learning strategies for credit cards fraud detection: Extended abstract,” Proc. - 2020 IEEE 7th Int. Conf. Data Sci. Adv. Anal. DSAA 2020, pp. 785–786, Oct. 2020.
[59] Y. Kim, “Convolutional neural networks for sentence classification,” in EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, 2014, pp. 1746–1751.
[60] E. Pejhan and M. Ghasemzadeh, “Multi-Sentence Hierarchical Generative Adversarial Network GAN (MSH-GAN) for Automatic Text-to-Image Generation,” J. AI Data Min., vol. 9, no. 4, pp. 475–485, Nov. 2021.
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