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Application of Machine Learning Models in Fraud Detection in Financial Transactions

By
Roberto Carlos Dávila-Morán ,
Roberto Carlos Dávila-Morán

Universidad Continental (UC), Facultad de Ingeniería, Carrera de Ingeniería Industrial. Ciudad de Huancayo, Perú

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Rafael Alan Castillo-Sáenz ,
Rafael Alan Castillo-Sáenz

Universidad San Ignacio de Loyola (USIL), Facultad de Ciencias Empresariales, Carrera de International Business. Ciudad de Lima, Perú

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Alfonso Renato Vargas-Murillo ,
Alfonso Renato Vargas-Murillo

Universidad Privada del Norte (UPN), Facultad de Derecho y Ciencias Políticas, Carrera de Derecho. Ciudad de Lima, Perú

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Leonardo Velarde Dávila ,
Leonardo Velarde Dávila

Universidad Peruana de Ciencias Aplicadas (UPC). Facultad de Negocios, Carrera de Administración. Ciudad de Lima, Perú.

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Elvira García-Huamantumba ,
Elvira García-Huamantumba

Universidad Privada Norbert Wiener (UPNW), Facultad de Ingeniería y Negocios, Carrera de Administración y Negocios Internacionales. Ciudad de Lima, Perú

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Camilo Fermín García-Huamantumba ,
Camilo Fermín García-Huamantumba

Universidad Privada Norbert Wiener (UPNW), Facultad de Ingeniería y Negocios, Carrera de Administración y Negocios Internacionales. Ciudad de Lima, Perú

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Renzo Fidel Pasquel Cajas ,
Renzo Fidel Pasquel Cajas

Universidad Nacional Hermilio Valdizán (UNHEVAL), Escuela de Posgrado, Ciudad de Huánuco, Perú

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Carlos Enrique Guanilo Paredes ,
Carlos Enrique Guanilo Paredes

Universidad Autónoma del Perú (UA), Facultad de Ciencias de la Gestión y Comunicaciones, Carrera de Administración de Empresas, Ciudad de Lima, Perú

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Abstract

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.

How to Cite

1.
Dávila-Morán RC, Castillo-Sáenz RA, Vargas-Murillo AR, Velarde Dávila L, García-Huamantumba E, García-Huamantumba CF, Pasquel Cajas RF, Guanilo Paredes CE. Application of Machine Learning Models in Fraud Detection in Financial Transactions. Data and Metadata [Internet]. 2023 Oct. 29 [cited 2024 Feb. 28];2:109. Available from: https://dm.saludcyt.ar/index.php/dm/article/view/109

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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