Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection
Although the incidence of credit card fraud is limited to a small percentage of transactions, the related financial losses may be huge. This demands the design of automatic Fraud Detection Systems (FDS) able to detect fraudulent transactions with high precision and deal with the heterogeneous nature of the fraudster behavior. Indeed, the nature of the fraud behavior may strongly differ according to the payment system (e.g. e-commerce or shop terminal), the country and the population segment. Given the high cost of designing data-driven FDSs, it is more and more important for transactional companies to reuse existing pipelines and adapt them to different domains and contexts: this boils down to a well-known problem of transfer learning.
This paper deals with deep transfer learning approaches for credit card fraud detection and focuses on transferring classification models learned on a specific category of transactions (e-commerce) to another (face-to-face). In particular we present and discuss two domain adaptation techniques in a deep neural network setting: the first one is an original domain adaptation strategy relying on the creation of additional features to transfer properties of the source domain to the target one and the second is an extension of a recent work of Ganin et al.
The two methods are assessed, together with three state-of-the-art benchmarks, on a five-months dataset (more than 80 million e-commerce and face-to face transactions) provided by a major card issuer.
KeywordsFraud detection Domain adaptation Transfer learning
- 2.Ahmed, A., Yu, K., Xu, W., Gong, Y., Xing, E.: Training hierarchical feed-forward visual recognition models using transfer learning from pseudo-tasks. In: ECCV (3), pp. 69–82 (2008)Google Scholar
- 6.Chollet, F., et al.: Keras (2015). https://keras.io
- 7.Chopra, S., Balakrishnan, S., Gopalan, R.: DLID: deep learning for domain adaptation by interpolating between domains. In: ICML Workshop on Challenges in Representation Learning (2013)Google Scholar
- 8.Ciresan, D.C., Meier, U., Schmidhuber, J.: Transfer learning for Latin and Chinese characters with deep neural networks. In: IJCNN, pp. 1–6. IEEE (2012)Google Scholar
- 9.Dai, W., Yang, Q., Xue, G.R., Yu, Y.: Boosting for transfer learning. In: Proceedings of the 24th International Conference on Machine Learning, ICML 2007, pp. 193–200. ACM (2007)Google Scholar
- 10.Dal Pozzolo, A., Boracchi, G., Caelen, O., Alippi, C., Bontempi, G.: Credit card fraud detection and concept-drift adaptation with delayed supervised information. In: Proceedings of the International Joint Conference on Neural Networks, pp. 1–8. IEEE (2015)Google Scholar
- 13.Daume III, H.: Frustratingly easy domain adaptation. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pp. 256–263. Association for Computational Linguistics, Prague, Czech Republic, June 2007Google Scholar
- 17.Gao, J., Fan, W., Jiang, J., Han, J.: Knowledge transfer via multiple model local structure mapping. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008, pp. 283–291. ACM, New York (2008)Google Scholar
- 18.Glorot, X., Bordes, A., Bengio, Y.: Domain adaptation for large-scale sentiment classification: a deep learning approach. In: Proceedings of the Twenty-eight International Conference on Machine Learning, ICML (2011)Google Scholar
- 20.HSN Consultants, Inc.: The Nilson report (consulted on 2018-10-23) (2017). https://nilsonreport.com/upload/content_promo/The_Nilson_Report_Issue_1118.pdf
- 22.Lebichot, B., Braun, F., Caelen, O., Saerens, M.: A graph-based, semi-supervised, credit card fraud detection system, pp. 721–733. Springer, Cham (2017)Google Scholar
- 24.Margolis, A.: A literature review of domain adaptation with unlabeled data. Technical report, University of Washington (2011)Google Scholar
- 28.Tan, S., Cheng, X., Wang, Y., Xu, H.: Adapting Naive Bayes to domain adaptation for sentiment analysis. In: Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval, ICML 2009, pp. 337–349. Springer (2009)Google Scholar