<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1665-5346</journal-id>
<journal-title><![CDATA[Revista mexicana de economía y finanzas]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. mex. econ. finanz]]></abbrev-journal-title>
<issn>1665-5346</issn>
<publisher>
<publisher-name><![CDATA[Instituto Mexicano de Ejecutivos de Finanzas A.C.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1665-53462024000400010</article-id>
<article-id pub-id-type="doi">10.21919/remef.v19i4.868</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Predicción del riesgo crediticio a microfinanciera usando aprendizaje computacional]]></article-title>
<article-title xml:lang="en"><![CDATA[Microfinance Credit Risk Prediction Using Computational Learning]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Melchor Pérez]]></surname>
<given-names><![CDATA[Erwis]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ramírez Guzmán]]></surname>
<given-names><![CDATA[Moisés Emmanuel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hernández Jiménez]]></surname>
<given-names><![CDATA[Araceli]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Santiago Alvarado]]></surname>
<given-names><![CDATA[Agustín]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Tecnológica de la Mixteca  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad del Istmo  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2024</year>
</pub-date>
<volume>19</volume>
<numero>4</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1665-53462024000400010&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1665-53462024000400010&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1665-53462024000400010&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El principal riesgo que enfrentan las Sociedades Cooperativas de Ahorro y Préstamo según la Comisión Nacional Bancaria y de Valores, es el crédito. En este artículo se aplican modelos híbridos de aprendizaje computacional para la predicción del riesgo crediticio de solicitudes de clientes pertenecientes a estas sociedades, además se describe la importancia de la selección de características y la reducción de la dimensionalidad, combinando métodos de aprendizaje no supervisado y supervisado. Los experimentos mostraron que los modelos híbridos en conjunto con técnicas de selección de características superan a los algoritmos de aprendizaje computacional de manera individual utilizando todas las características de los conjuntos de datos analizados. Los conjuntos están desbalanceados, por lo cual se utiliza el método de SMOTE para sobremuestrear la clase minoritaria y equilibrar la cantidad de elementos durante el entrenamiento. Los resultados obtenidos confirman que la combinación de métodos no supervisados y supervisados generan una mejora del 6% en el accuracy en comparación con los modelos del estado del arte y 10% en la reducción del error del tipo II para las bases de datos públicas analizadas.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract According to the National Banking and Securities Commission, the main risk faced by Savings and Loan Cooperative Societies is credit. This paper applies hybrid computational learning models to predict the credit risk of applications from customers belonging to these societies, and describes the importance of feature selection and dimensionality reduction, combining unsupervised and supervised learning methods. Experiments showed that hybrid models in conjunction with feature selection techniques outperform computational learning algorithms individually using all the features of the analyzed data sets. The data sets are unbalanced, so the SMOTE method is used to oversample the minority class and balance the number of features during training. The results obtained confirm that the combination of unsupervised and supervised methods generate a 6% improvement in accuracy compared to the state of the art models and 10% reduction in type II error for the analyzed public databases.]]></p></abstract>
<kwd-group>
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<kwd lng="es"><![CDATA[C52]]></kwd>
<kwd lng="es"><![CDATA[C53]]></kwd>
<kwd lng="es"><![CDATA[Instituciones Microfinancieras]]></kwd>
<kwd lng="es"><![CDATA[Redes neuronales]]></kwd>
<kwd lng="es"><![CDATA[Árbol de decisión]]></kwd>
<kwd lng="es"><![CDATA[XGBoost]]></kwd>
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<kwd lng="en"><![CDATA[C22]]></kwd>
<kwd lng="en"><![CDATA[C44]]></kwd>
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<kwd lng="en"><![CDATA[C52]]></kwd>
<kwd lng="en"><![CDATA[C53]]></kwd>
<kwd lng="en"><![CDATA[Microfinance institutions]]></kwd>
<kwd lng="en"><![CDATA[Neuronal networks]]></kwd>
<kwd lng="en"><![CDATA[Decision tree]]></kwd>
<kwd lng="en"><![CDATA[XGBoost]]></kwd>
<kwd lng="en"><![CDATA[SMOTE]]></kwd>
</kwd-group>
</article-meta>
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