<?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>2218-3620</journal-id>
<journal-title><![CDATA[Revista Universidad y Sociedad]]></journal-title>
<abbrev-journal-title><![CDATA[Universidad y Sociedad]]></abbrev-journal-title>
<issn>2218-3620</issn>
<publisher>
<publisher-name><![CDATA[Editorial "Universo Sur"]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2218-36202025000400004</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Assessing operational risks in modern financial and credit institutions]]></article-title>
<article-title xml:lang="es"><![CDATA[Evaluación de los riesgos operativos en instituciones financieras y crediticias modernas]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Makhov]]></surname>
<given-names><![CDATA[Ilia]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Chumakova]]></surname>
<given-names><![CDATA[Ekaterina]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Korneev]]></surname>
<given-names><![CDATA[Dmitry]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gasparian]]></surname>
<given-names><![CDATA[Mikhail]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Filyuk]]></surname>
<given-names><![CDATA[Mariya]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Plekhanov Russian University of Economics  ]]></institution>
<addr-line><![CDATA[ Moscow]]></addr-line>
<country>Russia</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>17</volume>
<numero>4</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2218-36202025000400004&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2218-36202025000400004&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2218-36202025000400004&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT The article considers modern methods for assessing the criticality of operational risks arising from technological system failures, human errors, and organizational shortcomings. Traditional approaches, such as regression and clustering, have proven inadequate for analyzing the nonlinear and volatile nature of operational risks in the context of digital transformation. To address these challenges, an innovative approach is proposed, leveraging neural network ensembles and explainable artificial intelligence (XAI) technologies. This approach enhances the accuracy and interpretability of criticality risk forecasts. The article presents the results of previous studies in which multi-layer perceptrons (DNNs) and radial basis function networks (RBFNs) were tested for managing operational risks in credit institutions. These models demonstrated high accuracy in assessing the criticality of risks associated with human errors, Informatic Technology (IT) failures, and business process disruptions. DNNs proved effective in analyzing complex data interrelationships, while RBFNs showed high performance in classifying IT incidents. Based on these results, the further development of models using neural network ensembles is proposed to improve forecast accuracy and resilience to new data. XAI methods, such as LIME and Grad-CAM, are applied to interpret model outcomes, ensuring transparency and trust in decision-making processes. The article also outlines directions for future research and practical steps for implementing the proposed approaches in operational risk management systems.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN El artículo analiza métodos modernos para evaluar la criticidad de los riesgos operativos derivados de fallos de sistemas tecnológicos, errores humanos y deficiencias organizacionales. Enfoques tradicionales, como la regresión y la agrupación en clústeres, han demostrado ser inadecuados para analizar la naturaleza no lineal y volátil de los riesgos operativos en el contexto de la transformación digital. Para abordar estos desafíos, se propone un enfoque innovador que aprovecha conjuntos de redes neuronales y tecnologías de inteligencia artificial explicable (XAI). Este enfoque mejora la precisión e interpretabilidad de los pronósticos de riesgos de criticidad. El artículo presenta los resultados de estudios previos en los que se probaron perceptrones multicapa (DNN) y redes de función de base radial (RBFN) para la gestión de riesgos operativos en entidades de crédito. Estos modelos demostraron una alta precisión en la evaluación de la criticidad de los riesgos asociados a errores humanos, fallos de Tecnología Informática (TI) e interrupciones de los procesos de negocio. Las DNN demostraron ser eficaces en el análisis de interrelaciones complejas de datos, mientras que las RBFN mostraron un alto rendimiento en la clasificación de incidentes de TI. Con base en estos resultados, se propone el desarrollo de modelos que utilicen conjuntos de redes neuronales para mejorar la precisión de los pronósticos y la resiliencia a nuevos datos. Se aplican métodos XAI, como LIME y Grad-CAM, para interpretar los resultados de los modelos, garantizando la transparencia y la confianza en los procesos de toma de decisiones. El artículo también describe las líneas de investigación futuras y los pasos prácticos para implementar los enfoques propuestos en los sistemas de gestión de riesgos operacionales.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Operational risks]]></kwd>
<kwd lng="en"><![CDATA[Risk criticality]]></kwd>
<kwd lng="en"><![CDATA[Neural network ensembles]]></kwd>
<kwd lng="en"><![CDATA[Explainable artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[Risk management]]></kwd>
<kwd lng="es"><![CDATA[Riesgos operacionales]]></kwd>
<kwd lng="es"><![CDATA[Criticidad del riesgo]]></kwd>
<kwd lng="es"><![CDATA[Conjuntos de redes neuronales]]></kwd>
<kwd lng="es"><![CDATA[Inteligencia artificial explicable]]></kwd>
<kwd lng="es"><![CDATA[Gestión de riesgos]]></kwd>
</kwd-group>
</article-meta>
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