Inteligencia Artificial y Auditoría: Tendencias de la literatura científica

Objetivos: 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...

Full description

Autores:
Fajardo Pereira, Johana
Toscano Hernández, Aníbal
García Alarcón, Héctor
Llanos Ayola, Jones
Tipo de recurso:
Article of journal
Fecha de publicación:
2023
Institución:
Universidad de Cartagena
Repositorio:
Repositorio Universidad de Cartagena
Idioma:
spa
OAI Identifier:
oai:repositorio.unicartagena.edu.co:11227/17931
Acceso en línea:
https://hdl.handle.net/11227/17931
https://doi.org/10.32997/pe-2023-4575
Palabra clave:
Accounting
Artificial Intelligence
Automation
Digitalization
Financial Auditing
Auditoría financiera
Automatización
Contabilidad
Digitalización
Inteligencia Artificial
Rights
openAccess
License
https://creativecommons.org/licenses/by-nc-nd/4.0
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oai_identifier_str oai:repositorio.unicartagena.edu.co:11227/17931
network_acronym_str UCART2
network_name_str Repositorio Universidad de Cartagena
repository_id_str
dc.title.spa.fl_str_mv Inteligencia Artificial y Auditoría: Tendencias de la literatura científica
dc.title.translated.eng.fl_str_mv Artificial Intelligence and Auditing: Trends in scientific literature
title Inteligencia Artificial y Auditoría: Tendencias de la literatura científica
spellingShingle Inteligencia Artificial y Auditoría: Tendencias de la literatura científica
Accounting
Artificial Intelligence
Automation
Digitalization
Financial Auditing
Auditoría financiera
Automatización
Contabilidad
Digitalización
Inteligencia Artificial
title_short Inteligencia Artificial y Auditoría: Tendencias de la literatura científica
title_full Inteligencia Artificial y Auditoría: Tendencias de la literatura científica
title_fullStr Inteligencia Artificial y Auditoría: Tendencias de la literatura científica
title_full_unstemmed Inteligencia Artificial y Auditoría: Tendencias de la literatura científica
title_sort Inteligencia Artificial y Auditoría: Tendencias de la literatura científica
dc.creator.fl_str_mv Fajardo Pereira, Johana
Toscano Hernández, Aníbal
García Alarcón, Héctor
Llanos Ayola, Jones
dc.contributor.author.spa.fl_str_mv Fajardo Pereira, Johana
Toscano Hernández, Aníbal
García Alarcón, Héctor
Llanos Ayola, Jones
dc.subject.eng.fl_str_mv Accounting
Artificial Intelligence
Automation
Digitalization
Financial Auditing
topic Accounting
Artificial Intelligence
Automation
Digitalization
Financial Auditing
Auditoría financiera
Automatización
Contabilidad
Digitalización
Inteligencia Artificial
dc.subject.spa.fl_str_mv Auditoría financiera
Automatización
Contabilidad
Digitalización
Inteligencia Artificial
description Objetivos: 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.
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-04-14T00:00:00Z
2024-09-05T20:24:32Z
dc.date.available.none.fl_str_mv 2023-04-14T00:00:00Z
2024-09-05T20:24:32Z
dc.date.issued.none.fl_str_mv 2023-04-14
dc.type.spa.fl_str_mv Artículo de revista
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dc.type.local.eng.fl_str_mv Journal article
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spelling Fajardo Pereira, JohanaToscano Hernández, AníbalGarcía Alarcón, HéctorLlanos Ayola, Jones2023-04-14T00:00:00Z2024-09-05T20:24:32Z2023-04-14T00:00:00Z2024-09-05T20:24:32Z2023-04-140122-8900https://hdl.handle.net/11227/1793110.32997/pe-2023-45752463-0470https://doi.org/10.32997/pe-2023-4575Objetivos: 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.Background 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.application/pdfspaUniversidad de CartagenaPanorama Económicohttps://revistas.unicartagena.edu.co/index.php/panoramaeconomico/article/download/4575/3571187216031Al-Sayyed, S.M.; Al-Aroud, S. F.; Zayed, L. M., (2021). The effect of artificial intelligence technologies on audit evidence. 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