<?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>2448-6655</journal-id>
<journal-title><![CDATA[Análisis económico]]></journal-title>
<abbrev-journal-title><![CDATA[Anál. econ.]]></abbrev-journal-title>
<issn>2448-6655</issn>
<publisher>
<publisher-name><![CDATA[Universidad Autónoma Metropolitana, Unidad Azcapotzalco, División de Ciencias Sociales y Humanidades]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2448-66552025000200175</article-id>
<article-id pub-id-type="doi">10.24275/uam/azc/dcsh/ae/2025v40n104/segovia</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Redes neuronales artificiales en finanzas: evaluación de su utilidad en proyecciones]]></article-title>
<article-title xml:lang="en"><![CDATA[Artificial neural networks in finance: assessing their usefulness in projections]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Segovia Hernández]]></surname>
<given-names><![CDATA[Darío Alejandro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Madrid Paredones]]></surname>
<given-names><![CDATA[Rosa Marina]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gómez Medina]]></surname>
<given-names><![CDATA[Luis Enrique]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Veracruzana Instituto de Investigaciones y Estudios Superiores de las Ciencias Administrativas ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Veracruzana Instituto de Investigaciones y Estudios Superiores de las Ciencias Administrativas ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Veracruzana Instituto de Investigaciones y Estudios Superiores de las Ciencias Administrativas ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>08</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>08</month>
<year>2025</year>
</pub-date>
<volume>40</volume>
<numero>104</numero>
<fpage>175</fpage>
<lpage>187</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2448-66552025000200175&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2448-66552025000200175&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2448-66552025000200175&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Este trabajo analiza el papel de las redes neuronales artificiales (RNA) en las finanzas, centrándose en su capacidad para generar proyecciones financieras. Se revisan estudios teóricos y casos prácticos que demuestran la efectividad de las RNA en el procesamiento de grandes volúmenes de datos y en la adaptación a cambios, lo que las hace valiosas para pronósticos financieros. A través de un análisis exhaustivo de estudios recientes, se comparan diferentes enfoques de RNA con métodos tradicionales. Los casos de estudio incluyen la predicción de quiebras empresariales, la optimización de portafolios de inversión y la estimación de precios de activos como el petróleo. Se concluye que, a pesar de ciertos desafíos, las RNA ofrecen ventajas significativas en la generación de proyecciones financieras, mejorando tanto la precisión como la eficiencia en la toma de decisiones.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract This article analyzes the role of artificial neural networks (ANN) in finance, focusing on their ability to generate financial projections. The study examines theoretical frameworks and practical cases that demonstrate the effectiveness of ANN in processing large amounts of data and adapting to change, making them valuable for financial forecasting. A comprehensive analysis of recent studies is used to compare different ANN approaches with traditional methods. Case studies include the prediction of corporate bankruptcies, the optimization of investment portfolios and the estimation of asset prices such as oil. The conclusion is that despite certain challenges, ANN offer significant advantages in financial forecasting and improve both the accuracy and efficiency of decision making.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Redes neuronales artificiales]]></kwd>
<kwd lng="es"><![CDATA[Pronósticos]]></kwd>
<kwd lng="es"><![CDATA[Finanzas]]></kwd>
<kwd lng="es"><![CDATA[Planeación]]></kwd>
<kwd lng="en"><![CDATA[Financial inclusion]]></kwd>
<kwd lng="en"><![CDATA[Financial policy]]></kwd>
<kwd lng="en"><![CDATA[Economic growth]]></kwd>
<kwd lng="en"><![CDATA[Public policy evaluation]]></kwd>
<kwd lng="en"><![CDATA[Financial inclusion variables]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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