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

Resumo

Hoje vivemos uma mudança de paradigma no setor financeiro, com forte redução das agências bancárias físicas e aumento de serviços online. Contudo, a facilidade de abertura de contas digitais propiciada por esta mudança de paradigma também tem levado a um aumento nos casos de fraude. Este trabalho apresenta o problema de detecção de fraude financeira sob uma nova taxonomia e, também, investiga técnicas de classificação hierárquica para a tarefa. A abordagem hierárquica global (CLUS-HMC), em que toda a hierarquia de classes é considerada pelo classificador, resultou em melhores valores de Recall para as classes fraudulentas (33.31% para classe E e 35.09% para classe F), indicando um caminho de pesquisa promissor.

Referências

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.