Fraudulent Account Detection Using Hierarchical Classification

• Andressa O. Souza UFOP
• Mariana Mota UFOP
• Helen C. S. C. Lima UFOP
• Wellington Souza Gerencianet S.A
• Marcos Nicolau Gerencianet S.A
• Gladston Moreira UFOP
• Eduardo J. S. Luz UFOP

DOI: https://doi.org/10.5753/eniac.2022.227330

Abstract

Nowadays, we have been experiencing a paradigm change in the financial sector, with a high decrease in physical bank branches and an increase in online services. However, the easy opening of accounts provided by this change also increased fraud cases. This work presents the financial fraud detection problem under a new taxonomy and investigates hierarchical classification techniques for the task. The global hierarchical approach (CLUS-HMC), whereby the classifier considers the entire class hierarchy, resulted in better Recall values for fraudulent classes (33.31% for class E and 35.09% for class F), evidencing a promising research path.

References

Benford, F. (1938). The law of anomalous numbers. Proceedings of the American Philosophical Society, 78(4):551-572.

Borges, H. B., Silla, C. N., and Nievola, J. C. (2013). An evaluation of global-model hierarchical classification algorithms for hierarchical classification problems with single path of labels. Computers & Mathematics with Applications, 66(10):1991-2002. ICNC-FSKD 2012.

Breiman, L. (2001). Random forests. Machine Learning, 45(1):5-32.

Devi, D., Biswas, S. K., and Purkayastha, B. (2019). A cost-sensitive weighted random forest technique for credit card fraud detection. In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pages 1-6.

Drezewski, R., Sepielak, J., and Filipkowski, W. (2015). The application of social network analysis algorithms in a system supporting money laundering detection. Information Sciences, 295:18-32.

Freitas, A. and de Carvalho, A. (2007). A tutorial on hierarchical classification with applications in bioinformatics. Research and Trends in Data Mining Technologies and Applications.

Han, H., Wang, W.-Y., and Mao, B.-H. (2005). Borderline-smote: a new over-sampling method in imbalanced data sets learning. In International conference on intelligent computing, pages 878-887. Springer.

Li, C., Ding, N., Zhai, Y., and Dong, H. (2021). Comparative study on credit card fraud detection based on different support vector machines. Intelligent Data Analysis, 25:105-119.

Niu, X., Wang, L., and Yang, X. (2019). A comparison study of credit card fraud detection: Supervised versus unsupervised.

Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). "why should I trust you?": Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016, pages 1135-1144.

Roy, A., Sun, J., Mahoney, R., Alonzi, L., Adams, S., and Beling, P. (2018). Deep learning detecting fraud in credit card transactions. In 2018 Systems and Information Engineering Design Symposium (SIEDS), pages 129-134.

Shenvi, P., Samant, N., Kumar, S., and Kulkarni, V. (2019). Credit Card Fraud Detection using Deep Learning. In 2019 IEEE 5th International Conference for Convergence in Technology (I2CT), pages 1-5.

Vens, C., Struyf, J., Schietgat, L., Džeroski, S., and Blockeel, H. (2008). Decision trees for hierarchical multi-label classification. Machine Learning, 73:185-214.

Xu, L., Skoularidou, M., Cuesta-Infante, A., and Veeramachaneni, K. (2019). Modeling tabular data using conditional gan. In NeurIPS.

Xuan, S., Liu, G., Li, Z., Zheng, L., Wang, S., and Jiang, C. (2018). Random forest for credit card fraud detection. In 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC), pages 1-6.

Zheng, W. and Zhao, H. (2020). Cost-sensitive hierarchical classification for imbalance classes. Applied Intelligence, 50(8):2328-2338.