Customer Behavior Analysis to Improve Detection of Fraudulent ‎Transactions using Deep Learning

سال انتشار: 1401
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 116

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شناسه ملی سند علمی:

JR_JADM-10-1_008

تاریخ نمایه سازی: 21 فروردین 1401

چکیده مقاله:

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.

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نویسندگان

F. Baratzadeh

Department of Computer Engineering, Alzahra University, Tehran, Iran.

Seyed M. H. Hasheminejad

Department of Computer Engineering, Alzahra University, Tehran, Iran.

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  • N. Carneiro, G. Figueira, and M. Costa, “A data mining-based ...
  • Nilson Report (۲۰۱۹), The Nilson Report, Issue ۱۱۶۴, November. Retrieved ...
  • X. S. E.W.T. Ngai, H. Yong., Y.H. Wong, and Y. ...
  • J. C. W. Jha.Sanjeev and G. Montserrat, “Employing transaction aggregation ...
  • J. D. J. Piotr., A.M. Niall, J.D. Hand, and C. ...
  • S. Kumar, V. Kumar-Solanki, S. K. Choudhary, A. Selamat, and ...
  • Haibo He and E. A. Garcia, “Learning from Imbalanced Data,” ...
  • S. J. S. P.K. Chan, W. Fan, and A.L. Prodromidis, ...
  • D. J. H. R.J. Bolton, “Unsupervised profiling methods for fraud ...
  • A. Eshghi and M. Kargari, “Introducing a new method for ...
  • U. Fiore, A. De Santis, F. Perla, P. Zanetti, and ...
  • I. J. Goodfellow et al., “Generative adversarial nets,” in Advances ...
  • D. Devarriya, C. Gulati, V. Mansharamani, A. Sakalle, and A. ...
  • Y. Heryadi and H. L. H. S. Warnars, “Learning temporal ...
  • A. Ullah, J. Ahmad, K. Muhammad, M. Sajjad, and S. ...
  • S. L. Oh, E. Y. K. Ng, R. S. Tan, ...
  • R. Zhao, R. Yan, J. Wang, and K. Mao, “Learning ...
  • M. Syeda, Y. Q. Zhang, and Y. Pan, “Parallel granular ...
  • N. Carneiro, G. Figueira, and M. Costa, “A data mining ...
  • S. Bhattacharyya, S. Jha, K. Tharakunnel, and J. C. Westland, ...
  • M. Carminati, R. Caron, F. Maggi, I. Epifani, and S. ...
  • A. Correa Bahnsen, D. Aouada, A. Stojanovic, and B. Ottersten, ...
  • M. F. A. Gadi, X. Wang, and A. P. Do ...
  • A. Dal Pozzolo, O. Caelen, Y.-A. Le Borgne, S. Waterschoot, ...
  • C. Whitrow, D. J. Hand, P. Juszczak, D. Weston, and ...
  • Y. Sahin, S. Bulkan, and E. Duman, “A cost-sensitive decision ...
  • S. Maes, S. Maes, K. Tuyls, B. Vanschoenwinkel, and B. ...
  • R. C. Chen, M. L. Chiu, Y. L. Huang, and ...
  • R. C. Chen, S. T. Luo, X. Liang, and V. ...
  • P. K. Chan, W. Fan, A. L. Prodromidis, and S. ...
  • R. Brause, T. Langsdorf, and M. Hepp, “Neural data mining ...
  • C. C. Chiu and C. Y. Tsai, “A web services-based ...
  • X. Zhang, Y. Han, W. Xu, and Q. Wang, “HOBA: ...
  • W. Lee, S. J. Stolfo, and K. W. Mok, “Adaptive ...
  • C. S. Hilas, “Designing an expert system for fraud detection ...
  • A. Kanavos, S. A. Iakovou, S. Sioutas, and V. Tampakas, ...
  • R. A. Becker, C. Volinsky, and A. R. Wilks, “Fraud ...
  • A. Sudjianto, S. Nair, M. Yuan, A. Zhang, D. Kern, ...
  • D. J. Hand, “Fraud detection in telecommunications and banking: Discussion ...
  • G. Widmer, “Learning in the presence of concept drift and ...
  • J. G. Moreno-Torres, T. Raeder, R. Alaiz-Rodríguez, N. V. Chawla, ...
  • A. Tsymbal, “The problem of concept drift: definitions and related ...
  • J. Gama, I. Zliobaite, A. Bifet, M. Pechenizkiy, and A. ...
  • S. Haykin and L. Li, “Nonlinear Adaptive Prediction of Nonstationary ...
  • R. Akbani, S. Kwek, and N. Japkowicz, “Applying support vector ...
  • E. Aleskerov, B. Freisleben, B. Rao, Cardwatch: A neural network-based ...
  • C. Bunkhumpornpat, K. Sinapiromsaran, and C. Lursinsap, “Safe-level-SMOTE: Safe-level-synthetic minority ...
  • T. Razooqi, K. Raahemifar, P. Khurana, and A. Abhari, “Credit ...
  • D. Lin, C. K. M. Lee, M. K. Siu, H. ...
  • Z. Nematzadeh, R. Ibrahim, and A. Selamat, “Improving class noise ...
  • D. Kalaivani and T. Arunkumar, “Multi- process prediction model for ...
  • S. Priya and R. A. Uthra, “Comprehensive analysis for class ...
  • M. Mohamad, A. Selamat, O. Krejcar, H. Fujita, and T. ...
  • H. Mehmood, P. Kostakos, M. Cortes, T. Anagnostopoulos, S. Pirttikangas, ...
  • B. Lebichot, G. M. Paldino, G. Bontempi, W. Siblini, L. ...
  • Y. Kim, “Convolutional neural networks for sentence classification,” in EMNLP ...
  • E. Pejhan and M. Ghasemzadeh, “Multi-Sentence Hierarchical Generative Adversarial Network ...
  • نمایش کامل مراجع