Bibliographic citations
López, J., (2021). Comparación de modelos de aprendizaje de máquina en la predicción del incumplimiento de pago en el sector de las microfinanzas [Pontificia Universidad Católica del Perú]. http://hdl.handle.net/20.500.12404/19546
López, J., Comparación de modelos de aprendizaje de máquina en la predicción del incumplimiento de pago en el sector de las microfinanzas []. PE: Pontificia Universidad Católica del Perú; 2021. http://hdl.handle.net/20.500.12404/19546
@mastersthesis{renati/1667946,
title = "Comparación de modelos de aprendizaje de máquina en la predicción del incumplimiento de pago en el sector de las microfinanzas",
author = "López Malca, Jiam Carlos",
publisher = "Pontificia Universidad Católica del Perú",
year = "2021"
}
The financial institutions dedicated to Microfinance offer their services to a target audience that, for the most part, has low economic resources and/or whose access to traditional banking systems is limited, these institutions to develop in an unfavorable context the risks of non-compliance in the payments are greater compared to traditional banking, therefore it is required to make a financial economic evaluation with a greater degree of detail, requiring for this purpose the participation of a business expert that based on information obtained and own expertise determine if the potential client will be a good payer, this way of evaluating a customer has evolved in the financial sector in recent years, this largely due to the application of technologies such as artificial intelligence and machine learning, offering a uniqueness that is the ability to learn from the data, demanding less effort and human participation mana, and yielding higher levels of accuracy. This article presents the results of the experimentation carried out with the following machine learning models: Logistic Regression, XGBoost, Random Forest, Gradient Boosting, Multilayer Perceptron (MLP) and deep learning algorithms for the prediction of non-payment, applying subsampling and oversampling balancing techniques, including the SMOTE technique, and the One Hot Encoding technique was applied for the treatment of categorical variables. The different models of machine learning were applied to a data set provided by a leading Peruvian institution in the microfinance sector, with the XGBoost model reporting the best results, with an accuracy of 97.53% and an F1-Score of 0.1278.
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