Abstract
Credit card is becoming more and more popular in financial transactions, at the same time frauds are also increasing. Conventional methods use rule-based expert systems to detect fraud behaviors, neglecting diverse situations, extreme imbalance of positive and negative samples. In this paper, we propose a CNN-based fraud detection framework, to capture the intrinsic patterns of fraud behaviors learned from labeled data. Abundant transaction data is represented by a feature matrix, on which a convolutional neural network is applied to identify a set of latent patterns for each sample. Experiments on real-world massive transactions of a major commercial bank demonstrate its superior performance compared with some state-of-the-art methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bhattacharyya, S., Jha, S., Tharakunnel, K., Westland, J.C.: Data mining for credit card fraud: a comparative study. Decis. Support Syst. 50(3), 602–613 (2011)
Ghosh, S., Reilly, D.L.: Credit card fraud detection with a neural-network. In: Proceedings of the Twenty-Seventh Hawaii International Conference on System Sciences, 1994, vol. 3, pp. 621–630. IEEE (1994)
Khandani, A.E., Kim, A.J., Lo, A.W.: Consumer credit-risk models via machine-learning algorithms. J. Banking Finan. 34(11), 2767–2787 (2010)
Kokkinaki, A.I.: On a typical database transactions: identification of probable frauds using machine learning for user profiling. In: Proceedings of Knowledge and Data Engineering Exchange Workshop, 1997, pp. 107–113. IEEE (1997)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Maes, S., Tuyls, K., Vanschoenwinkel, B., Manderick, B.: Credit card fraud detection using bayesian and neural networks. In: Proceedings of the 1st International Naiso Congress on Neuro Fuzzy Technologies, pp. 261–270 (2002)
Ravisankar, P., Ravi, V., Rao, G.R., Bose, I.: Detection of financial statement fraud and feature selection using data mining techniques. Decis. Support Syst. 50(2), 491–500 (2011)
Van Vlasselaer, V., Bravo, C., Caelen, O., Eliassi-Rad, T., Akoglu, L., Snoeck, M., Baesens, B.: Apate: A novel approach for automated credit card transaction fraud detection using network-based extensions. Decis. Support Syst. 75, 38–48 (2015)
Acknowledgements
The work was supported by the National Natural Science Foundation of China (61272251), the Key Basic Research Program of Shanghai Municipality, China (15JC1400103) and the National Natural Science Foundation of China (91420302).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Fu, K., Cheng, D., Tu, Y., Zhang, L. (2016). Credit Card Fraud Detection Using Convolutional Neural Networks. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9949. Springer, Cham. https://doi.org/10.1007/978-3-319-46675-0_53
Download citation
DOI: https://doi.org/10.1007/978-3-319-46675-0_53
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46674-3
Online ISBN: 978-3-319-46675-0
eBook Packages: Computer ScienceComputer Science (R0)