Clustering algorithms applied to fraud detection
Abstract
In a technological context, where data are generated exponentially, the financial analysis has gradually become more important to avoid large losses due to fraud. In this paper, we seek to find the segmentation of transactions, through clustering techniques, based on the existence of distinct patterns between legitimate and illegal financial transactions. For this purpose, algorithms were tested and compared in terms of performance, cluster validation, interpretation and understanding, with the last three criteria being used to formulate hypotheses. As a result, a spacial search reduction is expected so that possible frauds can be investigated.
Keywords:
Machine Learning, Data Science, High-Performance Computing
References
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Leite, R. A., Gschwandtner, T., Miksch, S., Kriglstein, S., Pohl, M., Gstrein, E., and Kuntner, J. (2017). Eva: Visual analytics to identify fraudulent events. IEEE transactions on visualization and computer graphics, 24(1):330-339.
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Nunes, B., Colliri, T., Lauretto, M., Liu, W., and Zhao, L. (2021). Anomaly detection in brazilian federal government purchase cards through unsupervised learning techniques. In Brazilian Conference on Intelligent Systems, pages 19-32. Springer.
Wang, D., Cui, P., and Zhu, W. (2016). Structural deep network embedding. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1225-1234.
Zaki, M. J., Meira Jr, W., and Meira, W. (2014). Data mining and analysis: fundamental concepts and algorithms. Cambridge University Press.
Zhou, J., Wei, J., and Xu, B. (2021). Customer segmentation by web content mining. Journal of Retailing and Consumer Services, 61:102588.
Ismaili, O. A., Lemaire, V., and Cornuéjols, A. (2014). A supervised methodology to measure the variables contribution to a clustering. In International Conference on Neural Information Processing, pages 159-166. Springer.
Kodinariya, T. M. and Makwana, P. R. (2013). Review on determining number of cluster in k-means clustering. International Journal, 1(6):90-95.
Leite, R. A., Gschwandtner, T., Miksch, S., Kriglstein, S., Pohl, M., Gstrein, E., and Kuntner, J. (2017). Eva: Visual analytics to identify fraudulent events. IEEE transactions on visualization and computer graphics, 24(1):330-339.
Letizio, K. J. (2021). Combating Financial Fraud: A Machine Learning Approach. PhD thesis, Utica College.
Nunes, B., Colliri, T., Lauretto, M., Liu, W., and Zhao, L. (2021). Anomaly detection in brazilian federal government purchase cards through unsupervised learning techniques. In Brazilian Conference on Intelligent Systems, pages 19-32. Springer.
Wang, D., Cui, P., and Zhu, W. (2016). Structural deep network embedding. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1225-1234.
Zaki, M. J., Meira Jr, W., and Meira, W. (2014). Data mining and analysis: fundamental concepts and algorithms. Cambridge University Press.
Zhou, J., Wei, J., and Xu, B. (2021). Customer segmentation by web content mining. Journal of Retailing and Consumer Services, 61:102588.
Published
2022-04-07
How to Cite
FURLANETTO, Gabriel Covello; CARVALHO, Veronica Oliveira de; BALDASSIN, Alexandro; MANACERO, Aleardo.
Clustering algorithms applied to fraud detection. In: REGIONAL SCHOOL OF HIGH PERFORMANCE COMPUTING FROM SÃO PAULO (ERAD-SP), 13. , 2022, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 29-32.
DOI: https://doi.org/10.5753/eradsp.2022.222234.
