Universidad Continental (UC), Facultad de Ingeniería, Carrera de Ingeniería Industrial. Ciudad de Huancayo, Perú
Universidad San Ignacio de Loyola (USIL), Facultad de Ciencias Empresariales, Carrera de International Business. Ciudad de Lima, Perú
Universidad Privada del Norte (UPN), Facultad de Derecho y Ciencias Políticas, Carrera de Derecho. Ciudad de Lima, Perú
Universidad Peruana de Ciencias Aplicadas (UPC). Facultad de Negocios, Carrera de Administración. Ciudad de Lima, Perú.
Universidad Privada Norbert Wiener (UPNW), Facultad de Ingeniería y Negocios, Carrera de Administración y Negocios Internacionales. Ciudad de Lima, Perú
Universidad Privada Norbert Wiener (UPNW), Facultad de Ingeniería y Negocios, Carrera de Administración y Negocios Internacionales. Ciudad de Lima, Perú
Universidad Nacional Hermilio Valdizán (UNHEVAL), Escuela de Posgrado, Ciudad de Huánuco, Perú
Universidad Autónoma del Perú (UA), Facultad de Ciencias de la Gestión y Comunicaciones, Carrera de Administración de Empresas, Ciudad de Lima, Perú
Introduction: Fraud detection in financial transactions has become a critical concern in today's financial landscape. Machine learning techniques have become a key tool for fraud detection given their ability to analyze large volumes of data and detect subtle patterns.
Objective: Evaluate the performance of machine learning techniques such as Random Forest and Convolutional Neural Networks to identify fraudulent transactions in real time.
Methods: A real-world data set of financial transactions was obtained from various institutions. Data preprocessing techniques were applied that include multiple imputation and variable transformation. Models such as Random Forest, Convolutional Neural Networks, Naive Bayes and Logistic Regression were trained and optimized. Performance was evaluated using metrics such as F1 score.
Results: Random Forests and Convolutional Neural Networks achieved an F1 score greater than 95% on average, exceeding the target threshold. Random Forests produced the highest average F1 score of 0.956. It was estimated that the models detected 45% of fraudulent transactions with low variability.
Conclusions: The study demonstrated the effectiveness of machine learning models, especially Random Forests and Convolutional Neural Networks, for accurate real-time fraud detection. Its high performance supports the application of these techniques to strengthen financial security. Future research directions are also discussed.
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