Tendencias de la inteligencia artificial y sistemas expertos en la contabilidad. Análisis cienciométrico

• Autores: Quispe Fernandez, Gabith Miriam, Ayaviri Nina, Dante, Figueroa Oquendo, Alfredo Eduardo, Coro Moyon, Dayanna Maribel, Villa Villa, Vicente Marlon
• Formato: artículo
• Fecha de Publicación: 2025
• Institución: Pontificia Universidad Católica del Perú
• Repositorio: PUCP-Institucional
• Lenguaje: español
• OAI Identifier: oai:repositorio.pucp.edu.pe:20.500.14657/205209
• Enlace del recurso: https://revistas.pucp.edu.pe/index.php/contabilidadyNegocios/article/view/31514/28162; http://hdl.handle.net/20.500.14657/205209; https://doi.org/10.18800/contabilidad.202502.006
• Nivel de acceso: acceso abierto
• Materia: Artificial intelligence; Expert systems; Accounting; Business; Inteligencia artificial; Sistemas expertos; Contabilidad; Empresas; Inteligência artificial; Sistemas especialistas; Contabilidade; https://purl.org/pe-repo/ocde/ford#5.02.04

The impact of technological advance of artificial intelligence (AI) and expert systems on business and accounting is major. The research aimed at understanding the application trend of AI and expert systems in business accounting through scientometric indicators based on the evolutionary paradigm and considering mental faculties and activities performed by human beings through computer systems and the theory of computation, including automated reasoning, theorem proof, expert systems, natural language processing, robotics, languages and environments, learning, neural networks, genetic algorithms (López, 2017), as well as emerging and future (general intelligence) cognitive and learning theories (Gómez & Hochel, 2019). PRISMA method, mixed approach and content analysis of 82 articles indexed in Scopus from 1980 to 2025 were used. Results evidence that between years 2019 and 2024 a significant growth of studies, automation of accounting tasks, development of predictive models and cost optimization through neural networks and automated learning algorithms were observed, especially in the United States and China. It is concluded that future research must include the development of hybrid models that combine fuzzy logic, neural networks and expert systems to improve the interpretation of financial data.