<?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>2665-0398</journal-id>
<journal-title><![CDATA[Aula Virtual]]></journal-title>
<abbrev-journal-title><![CDATA[Aula Virtual]]></abbrev-journal-title>
<issn>2665-0398</issn>
<publisher>
<publisher-name><![CDATA[Fundación Aula Virtual]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2665-03982026000102076</article-id>
<article-id pub-id-type="doi">10.5281/zenodo.20394963</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[EVALUACIÓN DEL MODELO RANDOM FOREST COMO HERRAMIENTA DE PREDICCIÓN DEL RIESGO CREDITICIO EN ESTUDIANTES UNIVERSITARIOS]]></article-title>
<article-title xml:lang="en"><![CDATA[EVALUATION OF THE RANDOM FOREST MODEL AS A TOOL FOR PREDICTING CREDIT RISK IN UNIVERSITY STUDENTS]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[León Velarde]]></surname>
<given-names><![CDATA[César Gerardo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Lino García]]></surname>
<given-names><![CDATA[Yenso Rodrigo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Solano Rosembertt]]></surname>
<given-names><![CDATA[Guillermo Victor]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional Federico Villarreal  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Peru</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Nacional Federico Villarreal  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Peru</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Nacional Federico Villarreal  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Peru</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2026</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2026</year>
</pub-date>
<volume>7</volume>
<numero>14</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://ve.scielo.org/scielo.php?script=sci_arttext&amp;pid=S2665-03982026000102076&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://ve.scielo.org/scielo.php?script=sci_abstract&amp;pid=S2665-03982026000102076&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://ve.scielo.org/scielo.php?script=sci_pdf&amp;pid=S2665-03982026000102076&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El riesgo crediticio para los estudiantes universitarios es uno de los problemas en aumento en el ambiente de baja inclusión financiera que se asocia con Perú. Muchos jóvenes recurren a préstamos informales o tienen dificultades para acceder al crédito formal. Para tales prestatarios, se aplica un algoritmo de aprendizaje automático para medir la evaluación del puntaje crediticio, donde el algoritmo Random Forest (RF) es popular debido a su capacidad de predicción y la complejidad de las variables. El propósito del estudio es investigar los factores relevantes del riesgo crediticio según el comportamiento socioeconómico, académico y financiero de los estudiantes universitarios peruanos; verificar la predicción hecha por el modelo RF en comparación con el modelo tradicional. El diseño del estudio adoptado fue cuantitativo, básico y no experimental de corte transversal. Se utilizaron cuestionarios e investigaciones de bases de datos para la recolección de datos. Para sostener el marco teórico, se utilizaron el diagrama de Pareto, el diagrama de Ishikawa como herramientas de análisis. Los datos fueron preprocesados y el modelo Random Forest fue entrenado con validación cruzada y precisión, recall, F1 como métricas. En cuanto a los resultados obtenidos, el modelo alcanzó una precisión del 78% en la clasificación del riesgo crediticio. Las variables clave fueron los ingresos familiares, el historial de pagos, el uso de la tarjeta de crédito y el rendimiento académico, lo que demuestra que Random Forest es un modelo fuerte de predicción de riesgo crediticio en comparación con las tecnologías tradicionales. Puede ser utilizado para mejorar la toma de decisiones financieras, disminuir la morosidad y proporcionar políticas de financiamiento más equitativas y seguras para los estudiantes universitarios.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Credit risk for university students is one of the growing problems in the context of low financial inclusion associated with Peru. Many young people resort to informal loans or face difficulties in accessing formal credit. For such borrowers, a machine learning algorithm is applied to measure credit score assessment, with the Random Forest algorithm being popular due to its predictive capacity and ability to handle complex variables. The purpose of the study was to investigate the relevant factors of credit risk according to the socioeconomic, academic, and financial behavior of Peruvian university students, and to verify the predictions made by the RF model compared to the traditional model. The study design adopted was quantitative, basic, and non-experimental with a cross-sectional approach. Questionnaires and database inquiries were used for data collection. To support the theoretical framework, Pareto diagrams, Ishikawa diagrams, and VOS viewer were applied as analysis tools. The data were preprocessed, and the Random Forest model was trained with cross-validation using accuracy, recall, and F1 as metrics. Regarding the results obtained, the model achieved 78% accuracy in credit risk classification. The key variables were family income, payment history, credit card usage, and academic performance, demonstrating that Random Forest is a robust model for predicting credit risk compared to traditional technologies. It can be used to improve financial decision-making, reduce delinquency, and provide fairer and safer financing policies for university students.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Riesgo crediticio]]></kwd>
<kwd lng="es"><![CDATA[machine learning]]></kwd>
<kwd lng="es"><![CDATA[random forest]]></kwd>
<kwd lng="es"><![CDATA[estudiantes universitarios]]></kwd>
<kwd lng="es"><![CDATA[factores socioeconómicos]]></kwd>
<kwd lng="en"><![CDATA[Credit risk]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[random forest]]></kwd>
<kwd lng="en"><![CDATA[university students]]></kwd>
<kwd lng="en"><![CDATA[socioeconomic factors]]></kwd>
</kwd-group>
</article-meta>
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