<?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>S1665-53462023000300001</article-id>
<article-id pub-id-type="doi">10.21919/remef.v18i3.886</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Loan Default Prediction: A Complete Revision of LendingClub]]></article-title>
<article-title xml:lang="es"><![CDATA[Predicción del default: Una revisión completa de LendingClub]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Núñez Mora]]></surname>
<given-names><![CDATA[José Antonio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Moncayo]]></surname>
<given-names><![CDATA[Pamela]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Franco]]></surname>
<given-names><![CDATA[Carlos]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Madrazo-Lemarroy]]></surname>
<given-names><![CDATA[Pilar]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Beltrán]]></surname>
<given-names><![CDATA[Jaime]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Tecnológico y de Estudios Superiores de Monterrey  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Anáhuac  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>09</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>09</month>
<year>2023</year>
</pub-date>
<volume>18</volume>
<numero>3</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1665-53462023000300001&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1665-53462023000300001&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1665-53462023000300001&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The study aims to determine a credit default prediction model using data from LendingClub. The model estimates the effect of the influential variables on the prediction process of paid and unpaid loans. We implemented the random forest algorithm to identify the variables with the most significant influence on payment or default, addressing nine predictors related to the borrower's credit and payment background. Results confirm that the model&#8217;s performance generates a F1 Macro Score that accomplishes 90% in accuracy for the evaluation sample. Contributions of this study include using the complete dataset of the entire operation of LendingClub available, to obtain transcendental variables for the classification and prediction task, which can be helpful to estimate the default in the person-to-person loan market. We can draw two important conclusions, first we confirm the Random Forest algorithm's capacity to predict binary classification problems based on performance metrics obtained and second, we denote the influence of traditional credit scoring variables on default prediction problems.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El objetivo del estudio es determinar un modelo de predicción de default crediticio usando la base de datos de LendingClub. La metodología consiste en estimar las variables que influyen en el proceso de predicción de préstamos pagados y no pagados utilizando el algoritmo Random Forest. El algoritmo define los factores con mayor influencia sobre el pago o el impago, generando un modelo reducido a nueve predictores relacionados con el historial crediticio del prestatario y el historial de pagos dentro de la plataforma. La medición del desempeño del modelo genera un resultado F1 Macro Score con una precisión mayor al 90% de la muestra de evaluación. Las contribuciones de este estudio incluyen, el haber utilizado la base de datos completa de toda la operación de LendingClub disponible, para obtener variables trascendentales para la tarea de clasificación y predicción, que pueden ser útiles para estimar la morosidad en el mercado de préstamos de persona a persona. Podemos sacar dos conclusiones importantes, primero confirmamos la capacidad del algoritmo Random Forest para predecir problemas de clasificación binaria en base a métricas de rendimiento obtenidas y segundo, denotamos la influencia de las variables tradicionales de puntuación de crédito en los problemas de predicción por defecto.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Random Forest]]></kwd>
<kwd lng="en"><![CDATA[P2P lending]]></kwd>
<kwd lng="en"><![CDATA[LendingClub]]></kwd>
<kwd lng="en"><![CDATA[SMOTE]]></kwd>
<kwd lng="en"><![CDATA[Fintech]]></kwd>
<kwd lng="en"><![CDATA[Default Prediction]]></kwd>
<kwd lng="es"><![CDATA[Random Forest]]></kwd>
<kwd lng="es"><![CDATA[Préstamos persona a persona]]></kwd>
<kwd lng="es"><![CDATA[LendingClub]]></kwd>
<kwd lng="es"><![CDATA[SMOTE]]></kwd>
<kwd lng="es"><![CDATA[Fintech]]></kwd>
<kwd lng="es"><![CDATA[Predicción del Default]]></kwd>
</kwd-group>
</article-meta>
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