Hybrid artificial intelligence: Application in the banking sector
Palabras clave:
Artificial intelligence, banking sector, financial operations, automation, efficiency
Resumen
The integration of smart technologies, from data mining to Artificial Intelligence, has revolutionized the way financial institutions manage and use information. This progress has driven the development of hybrid artificial intelligence solutions, which fuse neural networks, fuzzy logic, genetic algorithms and intelligent agents, improving efficiency and accuracy in finance. Key application areas include implementing machine learning for personalized financial services, using artificial intelligence to improve credit risk assessments, and automating operations for greater efficiency. This study aims to analyze the implementation of hybrid artificial intelligence in the banking sector. The findings suggest that machine learning significantly personalizes services, increasing customer satisfaction and retention. Artificial intelligence has refined credit risk assessment, reducing errors and improving accuracy, while AI-enabled automation has streamlined operations. In addition, artificial intelligence helps analyze trends and innovate products. The combination of traditional and data-driven artificial intelligence techniques was identified as offering significant competitive advantages for financial institutions.
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Luo, X., Qin, M. S., Fang, Z., & Qu, Z. (2021). Artificial Intelligence coaches for sales agents: Caveats and solutions. Journal of Marketing, 85(2), 14-32. https://doi.org/10.1177/0022242920956676
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Moposita, D. A., & Jordán, J. E. (2022). Chatbot una herramienta de atención al cliente en tiempos de COVID-19: Un acercamiento teórico. Uniandes Episteme, 9(3), 327-350. https://revista.uniandes.edu.ec/ojs/index.php/EPISTEME/article/view/2481
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Powles, J., & Hodson, H. (2017). Google DeepMind and healthcare in an age of algorithms. Health and Technology. 7, 351-367. https://doi.org/10.1007/s12553-017-0179-1
Prieto, A., Prieto, B., Martinez, E., Ros, E., Pelayo, F., Ortega, J., & Rojas, I. (2016). Neural networks: An overview of early research, current frameworks and new challenges. Neurocomputing, 214, 242-268. https://doi.org/10.1016/j.neucom.2016.06.014
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Shaheen, M., Shahbaz, M., Ur Rehman, Z., & Guergachi, A. (2010). Aplicaciones de la minería de datos en la exploración de hidrocarburos. Artificial Intelligence Review, 35(1), 1-18. https://doi.org/10.1007/s10462-010-9180-z
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Sivanandam, S. N., & Deepa, S. N. (2008). Introduction to Genetic Algorithms. Springer Berlin Heidelberg.
Susskind, R. (1988). Expert systems in law: A jurisprudential inquiry. Oxford University Press.
Tamir, D. E., Rishe, N. D., & Kandel, A. (2015). Complex fuzzy sets and complex fuzzy logic an overview of theory and applications. In D. Tamir, N. Rishe & A. Kandel (Eds.), Fifty years of fuzzy logic and its applications (Vol. 326, pp. 661-681). Springer. https://doi.org/10.1007/978-3-319-19683-1_31
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Publicado
2024-09-13
Cómo citar
Corral de La Mata, D., García de Blanes Sebastián, M., & Carvajal Camperos, M. (2024). Hybrid artificial intelligence: Application in the banking sector. Revista De Ciencias Sociales, 30(3), 22-36. https://doi.org/10.31876/rcs.v30i3.42674
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