Abstract
With the development of technology, e-commerce became an essential part of an individual’s life, where individuals could easily purchase and sell products over the internet. However, fraud attempts; specifically credit card fraudulent attacks are rapidly increasing. Cards may potentially be stolen; fake records are being used and credit cards are subject to being hacked. Artificial Intelligence techniques tackle these credit card fraud attacks, by identifying patterns that predict false transactions. Both Machine Learning and Deep Learning models are used to detect and prevent fraud attacks. Machine Learning techniques provide positive results only when the dataset is small and do not have complex patterns. In contrast, Deep Learning deals with huge and complex datasets. However, most of the existing studies on Deep Learning have used private datasets, and therefore, did not provide a broad comparative study. This paper aims to improve the detection of credit card fraud attacks using Long Short-Term Memory Recurrent Neural Network (LSTM RNN) with a public dataset. Our proposed model proved to be effective. It achieved an accuracy rate of 99.4% which is higher compared to other existing Machine and Deep Learning techniques.
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References
Inc. US Legal. Internet fraud law and legal definition
U.S. payment card fraud losses by type 2018 | statistic
Mohammed, M., Khan, M.B., Bashier, E.B.M.: Machine Learning: Algorithms and Applications. Crc Press, Boca Raton (2016)
Xuan, S., Liu, G., Li, Z., Zheng, L., Wang, S., Jiang, C.: Random forest for credit card fraud detection. In 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC), pp. 1–6. IEEE (2018)
Roy, A., Sun, J., Mahoney, R., Alonzi, L., Adams, S., Beling, P.: Deep learning detecting fraud in credit card transactions. In: Systems and Information Engineering Design Symposium (SIEDS), pp. 129–134 (2018)
Machine Learning: the Power and Promise of Computers That Learn by Example. The Royal Society, 2017, Machine Learning: the Power and Promise of Computers That Learn by Example. royalsociety.org/~/media/policy/projects/machine-learning/publications/machine-learning-report.pdf
Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning, vol. 1. MIT press, Cambridge (2016)
Lu, Y.: Deep Neural Networks and Fraud Detection (2017)
Gandhi, R.: Introduction to Sequence Models - RNN, Bidirectional RNN, LSTM, GRU. Towards Data Science, Towards Data Science, 26 June 2018. towardsdatascience.com/introduction-to-sequence-models-rnn-bidirectional-rnn-lstm-gru-73927ec9df15
Charleonnan, A.: Credit card fraud detection using RUS and MRN algorithms. In: Management and Innovation Technology International Conference (MITicon), 2016, pp. MIT-73. IEEE (2016)
Luo, C., Desheng, W., Dexiang, W.: A deep learning approach for credit scoring using credit default swaps. Eng. Appl. Artif. Intell. 65, 465–470 (2017)
Wang, C., Wang, Y., Ye, Z., Yan, L., Cai, W., Pan, S.: Credit card fraud detection based on whale algorithm optimized bp neural network. In: 2018 13th International Conference on Computer Science & Education (ICCSE), pp. 1–4. IEEE (2018)
Ghobadi, F., Rohani, M.: Cost sensitive modeling of credit card fraud using neural network strategy. In: International Conference of Signal Processing and Intelligent Systems (ICSPIS), pp. 1–5. IEEE (2016)
Zhang, X.-Y., Yin, F., Zhang, Y.-M., Liu, C.-L., Bengio, Y.: Drawing and recognizing Chinese characters with recurrent neural network. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 849–862 (2018)
Yogatama, D., Dyer, C., Ling, W., Blunsom, P.: Generative and discriminative text classification with recurrent neural networks. arXiv preprint arXiv:1703.01898 (2017)
Toderici, G., Vincent, D., Johnston, N., Hwang, S.J., Minnen, D., Shor, J., Covell, M.: Full resolution image compression with recurrent neural networks. In: CVPR, pp. 5435–5443 (2017)
Choi, K., Fazekas, G., Sandler, M., Cho, K.: Convolutional recurrent neural networks for music classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2392–2396. IEEE (2017)
Kaggle: Your home for data science. https://www.kaggle.com
Tom SweersCredit Card Fraud. Radboud University, Bachelor thesis Computer Science (2018)
Naik, H.: Credit card fraud detection for online banking transactions. Int. J. Res. Appl. Sci. Eng. Technol. 6(4), 4573–4577 (2018)
Behera, T.K., Panigrahi, S.: Credit card fraud detection using a neuro-fuzzy expert system. In: Computational Intelligence in Data Mining, pp. 835–843. Springer, Singapore (2017)
Save, P., Tiwarekar, P., Jain, K.N., Mahyavanshi, N.: A novel idea for credit card fraud detection using decision tree. Int. J. Comput. Appl. 161(13) (2017)
Carneiro, N., Figueira, G., Costa, M.: A data mining based system for credit-card fraud detection in e-tail. Decis. Support Syst. 95, 91–101 (2017)
Maniraj, S.P., Aditya, S., Shadab, A., Deep Sarkar, S.: Credit card fraud detection using machine learning and data science. Int. J. Eng. Res. Technol. (IJERT) 08(09), September 2019
Renstrom, M., Holmsten, T.: Fraud Detection on Unlabeled Data with Unsupervised Machine Learning. The Royal Institute of Technology (2018)
Pumsirirat, A., Yan, L.: Credit card fraud detection using deep learning based on auto-encoder and restricted boltzmann machine. Int. J. Adv. Comput. Sci. Appl. 9(1), 18–25 (2018)
Reshma, R.S.: Deep learning enabled fraud detection in credit card transactions. Int. J. Res. Sci. Innov. (IJRSI) V(VII), 111–115 (2018)
Kazemi, Z., Zarrabi, H.: Using deep networks for fraud detection in the credit card transactions. In: 2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI), pp. 0630–0633. IEEE (2017)
Al-Shabi, M.: Credit card fraud detection using autoencoder model in unbalanced datasets. J. Adv. Math. Comput. Sci. 33(5), 1–16 (2019). https://doi.org/10.9734/jamcs/2019/v33i530192
Fu, K., Cheng, D., Tu, Y., Zhang, L.: Credit card fraud detection using convolutional neural networks. In: International Conference on Neural Information Processing, pp. 483–490. Springer, Cham (2016)
Pozzolo, A.D., 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)
Financial Transaction Card Originated Messages, document ISO 8583-1 (2003). https://www.iso.org/standard/31628.html
Ackowledgment
We would like to acknowledge the Artificial Intelligence and Data Analytics (AIDA) Lab, Prince Sultan University, Riyadh, Saudi Arabia for supporting this work.
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Marie-Sainte, S.L., Alamir, M.B., Alsaleh, D., Albakri, G., Zouhair, J. (2020). Enhancing Credit Card Fraud Detection Using Deep Neural Network. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1229. Springer, Cham. https://doi.org/10.1007/978-3-030-52246-9_21
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