Abstract
Main challenge for e-commerce transaction fraud prevention is that fraud patterns are rather dynamic and diverse. This paper introduces two innovative methods, fraud islands (link analysis) and multi-layer machine learning model, which can effectively tackle the challenge of detecting diverse fraud patterns. Fraud Islands are formed using link analysis to investigate the relationships between different fraudulent entities and to uncover the hidden complex fraud patterns through the formed network. Multi-layer model is used to deal with the largely diverse nature of fraud patterns. Currently, the fraud labels are determined through different channels which are banks’ declination decision, manual review agents’ rejection decisions, banks’ fraud alert and customers’ chargeback requests. It can be reasonably assumed that different fraud patterns could be caught though different fraud risk prevention forces (i.e. bank, manual review team and fraud machine learning model). The experiments showed that by integrating few different machine learning models which were trained using different types of fraud labels, the accuracy of fraud decisions can be significantly improved.
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Kou, Y., Lu, C.T., Sirwongwattana, S., Huang, Y.P.: Survey of fraud detection techniques. In: IEEE International Conference on Networking, Sensing and Control, vol. 2, pp. 749–754. IEEE (2004)
Wang, S., Liu, C., Gao, X., Qu, H., Xu, W.: Session-based fraud detection in online e-commerce transactions using recurrent neural networks. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 241–252. Springer, Cham, September 2017
Şahin, Y.G., Duman, E.: Detecting credit card fraud by decision trees and support vector machines (2011)
Minastireanu, E.A., Mesnita, G.: An analysis of the most used machine learning algorithms for online fraud detection. Inform. Econ. 23(1), 5–16 (2019)
Omen Homepage. https://omen.sg/detect-fraud-in-real-time-with-graph-databases/. Accessed 13 June 2019
Shah, N., Lamba, H., Beutel, A., Faloutsos, C.: The many faces of link fraud. In: 2017 IEEE International Conference on Data Mining (ICDM), pp. 1069–1074. IEEE, November 2017
Sadowski, G., Rathle, P.: Fraud detection: discovering connections with graph databases. White Paper-Neo Technology-Graphs are Everywhere (2014)
Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM (JACM) 46(5), 604–632 (1999)
Jurczyk, P., Agichtein, E.: Discovering authorities in question answer communities by using link analysis. In: Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management, pp. 919–922. ACM, November 2007
Vanneschi, L., Castelli, M.: Multilayer perceptrons. Encycl. Bioinform. Comput. Biol. 1, 612–620 (2019)
Svozila, D., Kvasnickab, V., Pospichalb, J.: Introduction to multi-layer feed-forward neural networks. Chemometr. Intell. Lab. Syst. 39(1), 43–62 (1997)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: 22nd SIGKDD Conference on Knowledge Discovery and Data Mining (2016)
Volkovs, M., Yu, G., Poutanen, T.: Content-based neighbor models for cold start in recommender systems. In: Proceeding of RecSys Challenge 2017, Proceedings of the Recommender Systems Challenge 2017, Article no. 7 (2017)
“Machine Learning Challenge Winning Solutions” in “Awesome XGBoost”. https://github.com/dmlc/xgboost/tree/master/demo. Accessed 14 June 2019
Xia, Y., Liu, C., Da, B., Xie, F.: A novel heterogeneous ensemble credit scoring model based on bstacking approach. Expert. Syst. Appl. 93, 182–199 (2017)
Apache Spark Homepage. https://spark.apache.org/. Accessed 14 June 2019
Azure Machine Learning Service Homepage. https://azure.microsoft.com/en-in/services/machine-learning-service/. Accessed 14 June 2019
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Nanduri, J., Liu, YW., Yang, K., Jia, Y. (2020). Ecommerce Fraud Detection Through Fraud Islands and Multi-layer Machine Learning Model. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication. FICC 2020. Advances in Intelligent Systems and Computing, vol 1130. Springer, Cham. https://doi.org/10.1007/978-3-030-39442-4_41
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DOI: https://doi.org/10.1007/978-3-030-39442-4_41
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