MODELOS DE APRENDIZAJE AUTOMÁTICO CONTRA EL FRAUDE: UN ENFOQUE HÍBRIDO PARA PROTEGER BILLONES EN TRANSACCIONES

Josué Vladimir Galarza Tulcanazo; Pablo Andrés Trejo Tapia

doi:10.47187/perf.v1i33.324

Authors

Josué Vladimir Galarza Tulcanazo Universidad Central del Ecuador, Facultad de Ciencias Económicas, Estadística, Quito, Ecuador
Pablo Andrés Trejo Tapia Universidad Central del Ecuador, Facultad de Ciencias Económicas, Estadística, Quito, Ecuador

DOI:

https://doi.org/10.47187/perf.v1i33.324

Keywords:

Credit card fraud, Machine learning, Class imbalance, SMOTE and ADASYN, Voting Classifier, SHAP

Abstract

Credit card fraud is a contemporary problem that significantly affects banking and consumers, reporting global losses of $33.5 billion for 2022, with an increasing trend over the years. This work addresses this issue through the implementation of machine learning models, focusing on the design, evaluation, and improvement of fraudulent transaction identification with high precision and accuracy.

The developed models faced a significant class imbalance, for which techniques such as SMOTE and ADASYN were implemented, improving the representation of the minority class corresponding to fraud cases. Additionally, Principal Component Analysis (PCA) was used to reduce dimensionality and optimize computational performance.

The results demonstrated that, in terms of scalability and adaptability, the neural network model exhibited excellent performance with large datasets. For hybrid models, Voting Classifier was implemented, achieving an optimal balance between adaptability, precision, and efficiency by combining the strengths of various models. The system's interpretability was enhanced through the implementation of SHAP, allowing for the explanation of model decisions in fraudulent transaction detection.

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