The data mining and machine learning techniques for identifying fraud using private transport app
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Data de publicação
2020
Periódico
WMSCI 2020 - 24th World Multi-Conference on Systemics, Cybernetics and Informatics, Proceedings
Citações (Scopus)
0
Autores
Pracidelli L.
Lopes F.
Lopes F.
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© 2020 WMSCI 2020 - 24th World Multi-Conference on Systemics, Cybernetics and Informatics, Proceedings. All rights reserved.Fraud can be defined as an illicit activity applied with the intention of obtaining financial benefits without considering the consequences of this act. With the digital transformation over the years and the recent expansion made possible by the use of different applications, we can see a significant change of various businesses and the diversification of electronic payments that contributed to companies suffering even more from fraud, as well as the increase in credit card payments that contributed to this growth. The objective of this dissertation was to propose a fraud detection based on the identification of behavioral patterns in the race bases of an application transport company and to consider the construction of artifact that can minimize the problem using unsupervised and supervised algorithms based on machine learning techniques.
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Assuntos Scopus
Behavioral patterns , Credit card payment , Digital transformation , Electronic payment , Financial benefits , Machine learning techniques , Supervised algorithm , Transport companies