Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/49447
Title: Modelos de classificação em fraudes financeiras: comparação de desempenho em casos de crime de smurfing
Other Titles: Financial fraud classification models: performance comparison in smurfing crime cases
Authors: Lima, Renato Ribeiro de
Maziero, Erick Galani
Guimarães, Paulo Henrique Sales
Pires, Danilo Machado
Keywords: Fraudes financeiras
Machine learning
Smurfing
Segurança financeira
Financial frauds
Financial security
Issue Date: 25-Feb-2022
Publisher: Universidade Federal de Lavras
Citation: ALCINO, M. S. Modelos de classificação em fraudes financeiras: comparação de desempenho em casos de crime de smurfing. 2022. 84 p. Dissertação (Mestrado em Estatística e Experimentação Agropecuária) – Universidade Federal de Lavras, Lavras, 2022.
Abstract: The difficulty in identifying financial fraud is directly related to technological advances, as the new possibilities of forms of financial transactions, in turn, generate new forms of fraudulent agents to act. In this context, the aim of this study is to explore the theoretical construction of six machine learning (ML) models, in addition to comparing them through specific performance evaluation metrics. Furthermore, this work develops an algorithm to detect a type of financial crime known as smurfing. This algorithm does not use ML techniques, but aims to classify financial transactions as possible fraud through the analysis of pooled data. Given the impossibility of using real financial data, due to its confidentiality, this work is using simulated data. Two different scenarios were generated, both highly unbalanced, in which the behavior of financial fraud varies according to specific parameters. The chosen classification models were logistic model, Fuzzy Rule Based Systems, Artificial Neural Networks, Random Forest, Extreme Gradient Reinforcement and Support Vector Machine. The comparison of the models in the different scenarios was done through a combination of the metrics Area Under de Curve, Recall and Fb , once data are imbalanced. The results showed that the Random Forest and Extreme Gradient Boosting models had the best performances, therefore, it is believed that the use of such models in real data, even with different parameters, can help in tracking illegal financial transactions and identifying fraudsters
URI: http://repositorio.ufla.br/jspui/handle/1/49447
Appears in Collections:Estatística e Experimentação Agropecuária - Mestrado (Dissertações)



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