Arquitetura de Tempo Real e Modelo de Aprendizado de Máquina para Detecção de Fraudes de Cartão de Crédito

• Robson S. Santos UFC
• Robesvânia Araújo UFC
• Paulo A. L. Rego UFC
• José M. da S. M. Filho UFC
• Jarélio G. da S. Filho Sidi
• José D. C. Neto Sidi
• Nicksson C. A. de Freitas Sidi
• Emanuel B. Rodrigues UFC

DOI: https://doi.org/10.5753/sbseg.2023.233688

Abstract

Faced with the growing need for safe and efficient systems to detect fraud in credit card transactions in real time, this study proposes and evaluates a fraud detection approach based on machine learning, integrated into a realtime architecture of a virtual bank . The multilayer architecture used allows a clear division of responsibilities, resulting in optimized and efficient operations. With the use of the Random Forest machine learning model, the system is able to make accurate predictions about possible fraudulent transactions. This implementation provides robust and reliable results, with an accuracy of 99,98%, a precision of 99,97%, a recall of 100% and an F1-score of 99,88%, highlighting the potential of machine learning to significantly improve the detection effectiveness of cheats.

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