Publicación:
Inteligencia Artificial y Auditoría: Tendencias de la literatura científica

dc.contributor.authorFajardo Pereira, Johanaspa
dc.contributor.authorToscano Hernández, Aníbalspa
dc.contributor.authorGarcía Alarcón, Héctorspa
dc.contributor.authorLlanos Ayola, Jonesspa
dc.date.accessioned2023-04-14T00:00:00Z
dc.date.accessioned2024-09-05T20:24:32Z
dc.date.available2023-04-14T00:00:00Z
dc.date.available2024-09-05T20:24:32Z
dc.date.issued2023-04-14
dc.description.abstractObjetivos: La inteligencia artificial se ha establecido como una fuerza disruptiva en una amplia gama de industrias, incluida la auditoría. En la última década, la Inteligencia artificial ha demostrado su capacidad para automatizar tareas, identificar patrones complejos y mejorar la precisión de los procesos de auditoría. El propósito fundamental de este estudio resumir y exponer los estudios científicos de la investigación relacionada con la inteligencia artificial y la auditoría a nivel mundial. Métodos: Se realizo un análisis bibliométrico que abarca un período de 37 años, desde 1984 hasta 2022. Para analizar y presentar los resultados se utilizó el paquete de análisis bibliométrico Biblioshiny, soportado en el programa R Studio, así como en el software VOSviewer, teniendo en cuenta 306 artículos y revisiones de literatura. Este enfoque cuantitativo nos permitió identificar patrones y tendencias en la investigación. Resultados: Los resultados reflejan cambios importantes en el número de publicaciones anuales al registrar que el 70,91% de los documentos se publicaron en los últimos 7 años (2016 a 2022) y solo el 29,08% fue publicado en los 30 años comprendidos entre 1984 y 2015. Además, entre las 234 revistas científicas con publicaciones relacionadas, se identifican las ocho principales que concentran un 12.8% de las publicaciones y acumulan 12.5% de las citaciones. El clúster más numeroso, representado en color rojo, resaltando los 10 principales “audit”, “Audit Quality”, “Auditing”, “Big Data”, “Big Data Analytics”, “Blockchain”, “Computers”, “Data Mining”, “Decision Making”. Conclusión: Esta investigación permite caracterizar la producción científica relacionada con la inteligencia artificial y la auditoria considerando la evolución temporal, características generales, redes de investigación con autores e instituciones, así como los clústeres temáticos de mayor relevancia en este campo de estudio.spa
dc.description.abstractBackground and objectives: Artificial intelligence has established itself as a disruptive force in a wide range of industries, including auditing. Over the last decade, Artificial Intelligence has demonstrated its ability to automate tasks, identify complex patterns, and improve the accuracy of audit processes. The fundamental purpose of this study is to summarize and present the scientific studies of research related to artificial intelligence and auditing worldwide. Methods: A bibliometric analysis was carried out covering a period of 37 years, from 1984 to 2022. To analyze and present the results, the Biblioshiny bibliometric analysis package was used, supported by the R Studio program, as well as the VOSviewer software, taking into account 306 articles and literature reviews. This quantitative approach allowed us to identify patterns and trends in the research. Findings: The results reflect important changes in the number of annual publications by recording that 70.91% of the documents were published in the last 7 years (2016 to 2022) and only 29.08% were published in the 30 years between 1984 and 2015. Furthermore, among the 234 scientific journals with related publications, the eight main ones are identified, which concentrate 12.8% of the publications and accumulate 12.5% of the citations. The most numerous cluster, represented in red, highlighting the top 10 “audit”, “Audit Quality”, “Auditing”, “Big Data”, “Big Data Analytics”, “Blockchain”, “Computers”, “Data Mining”, “Decision Making. Conclusion: This research allows to characterize the scientific production related to artificial intelligence and auditing, considering the temporal evolution, general characteristics, research networks with authors and institutions, as well as the most relevant clusters in this field.eng
dc.format.mimetypeapplication/pdfspa
dc.identifier.doi10.32997/pe-2023-4575
dc.identifier.eissn2463-0470
dc.identifier.issn0122-8900
dc.identifier.urihttps://hdl.handle.net/11227/17931
dc.identifier.urlhttps://doi.org/10.32997/pe-2023-4575
dc.language.isospaspa
dc.publisherUniversidad de Cartagenaspa
dc.relation.bitstreamhttps://revistas.unicartagena.edu.co/index.php/panoramaeconomico/article/download/4575/3571
dc.relation.citationendpage187
dc.relation.citationissue2spa
dc.relation.citationstartpage160
dc.relation.citationvolume31spa
dc.relation.ispartofjournalPanorama Económicospa
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
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dc.rights.creativecommonsEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.spa
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0spa
dc.sourcehttps://revistas.unicartagena.edu.co/index.php/panoramaeconomico/article/view/4575spa
dc.subjectAccountingeng
dc.subjectArtificial Intelligenceeng
dc.subjectAutomationeng
dc.subjectDigitalizationeng
dc.subjectFinancial Auditingeng
dc.subjectAuditoría financieraspa
dc.subjectAutomatizaciónspa
dc.subjectContabilidadspa
dc.subjectDigitalizaciónspa
dc.subjectInteligencia Artificialspa
dc.titleInteligencia Artificial y Auditoría: Tendencias de la literatura científicaspa
dc.title.translatedArtificial Intelligence and Auditing: Trends in scientific literatureeng
dc.typeArtículo de revistaspa
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dc.type.coarversionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.localJournal articleeng
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTREFspa
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa
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