Evaluación de la resiliencia de modelos predictivos basados en IA en contextos de crisis económicas: una revisión sistemática
DOI:
https://doi.org/10.5281/zenodo.17382855Palabras clave:
crisis económicas, inteligencia artificial, resilienciaResumen
En un contexto de creciente incertidumbre económica, comprender cómo las crisis impactan la resiliencia de los modelos predictivos basados en inteligencia artificial (IA) es fundamental. Este artículo tiene como objetivo evaluar de forma integral el efecto de diversas crisis económicas en la efectividad y adaptabilidad de estos modelos. Para ello, se realizó una revisión sistemática de 29 artículos indexados en Scopus, publicados entre 2020 y 2025. La selección de estudios consideró criterios estrictos de inclusión, priorizando investigaciones revisadas por pares que abordaran problemáticas vinculadas a crisis financieras, sanitarias y sociales, descartando aquellos que no cumplían con dichos requisitos. Los resultados revelan que los modelos que incorporan variables macroeconómicas y contextuales presentan mayor robustez y precisión, destacándose por su capacidad para adaptarse a escenarios de alta volatilidad. Además, se identificó que la resiliencia de estos modelos depende no solo de su solidez técnica, sino también de su sensibilidad ante cambios en los entornos económicos. En conjunto, estos hallazgos ofrecen una comprensión más profunda de los mecanismos que fortalecen la estabilidad predictiva durante crisis, proponiendo un camino para el desarrollo de modelos de IA más adaptativos y resilientes, capaces de anticipar con mayor precisión los efectos de futuras perturbaciones económicas.
Descargas
Referencias
Abdullayev, I., Akhmetshin, E., Kosorukova, I., Klochko, E., Cho, W., & Joshi, G. P. (2024). Modeling of extended osprey optimization algorithm with Bayesian neural network: An application on Fintech to predict financial crisis. AIMS Mathematics, 9(7), 17555–17577. https://doi.org/10.3934/math.2024853
Addisu Kassie, B., & Bang, J. (2022). Is it recession-proof? Masstige purchase intention: The moderating effect of perceived economic crisis. SHS Web of Conferences, 132, 01013. https://doi.org/10.1051/shsconf/202213201013
Amamou, S., Daoud, M., & Bargaoui, S. (2024). Green bonds forecasting: Evidence from pre-crisis, COVID-19 and Russian–Ukrainian crisis frameworks. Journal of Economic Studies, 52(1), 179–193. https://doi.org/10.1108/jes-01-2024-0061
Ben Ayed, W., & Ben Hassen, R. (2024). The Basel 2.5 capital regulatory framework and the COVID-19 crisis: evidence from the ethical investment market. PSU Research Review: An International Journal, 8(3), 727–748. https://doi.org/10.1108/PRR-06-2022-0082
Bhattacharya, R., Kafila, Somanchi, H. K., Haralayya, B., Nagpal, P., & Chitsimran. (2023). Modified Grey Wolf Optimizer with Sparse Autoencoder for financial crisis prediction in small marginal firms. En Proceedings of the Second International Conference on Electronics and Renewable Systems (ICEARS-2023) (pp. 907-913). IEEE. https://doi.org/10.1109/ICEARS56392.2023.10085618
Chumachenko, D., Dudkina, T., Chumachenko, T., & Morita, P. P. (2023). Epidemiological implications of war: Machine learning estimations of the Russian invasion’s effect on Italy’s COVID-19 dynamics. Computation, 11(11), 221. https://doi.org/10.3390/computation11110221
Evgrafova, R., Ivashkovskaya, I., & Шорохова, Е. (2023). Corporate resilience in cancel culture times”, Journal of Corporate Finance Research / Корпоративные Финансы, 17(4), 19-37. https://doi.org/10.17323/j.jcfr.2073-0438.17.4.2023.19-37
Gîfu, D. (2025). Exploring the potential of e-learning in economic crisis prediction. Proceedings of the International Conference on Virtual Learning, 20, 144–152. https://www.researchgate.net/publication/390562045_Exploring_the_potential_of_e-learning_in_economic_crisis_prediction
Hamdi, M., & Chkili, W. (2019). An artificial neural network augmented GARCH model for Islamic stock market volatility: Do asymmetry and long memory matter? Economic Research Forum, (Working Paper No. 1325). https://erf.org.eg/publications/an-artificial-neural-network-augmented-garch-model-for-islamic-stock-market-volatility-do-asymmetry-and-long-memory-matter/
Iuga, I., & Mihalciuc, A. (2020). Major crises of the XXIst century and impact on economic growth. Sustainability, 12(22), 9373. https://doi.org/10.3390/su12229373
Karaev, A. K., & Borisova, O. V. (2025). Prospective models of financial forecasting of budget revenues. Finance: Theory and Practice, 29(1), 20-33. https://doi.org/10.26794/2587-5671-2025-29-1-20-33
Kim, T. H., Chinthaginjala, R., Srinivasulu, A. et al. (2025). COVID-19 health data prediction: a critical evaluation of CNN-based approaches. Scientific Reports, 15, 9121. https://doi.org/10.1038/s41598-025-92464-0
Kiranmai Balijepalli, N. S. S., & Thangaraj, V. (2025). Prediction of cryptocurrency’s price using ensemble machine learning algorithms. European Journal of Management and Business Economics. https://doi.org/10.1108/EJMBE-08-2023-0244
Korol, T., & Fotiadis, A. K. (2022). Implementing artificial intelligence in forecasting the risk of personal bankruptcies in Poland and Taiwan. Oeconomia Copernicana, 13(2), 407-438. https://doi.org/10.24136/oc.2022.013
Kotan, K., Kotan, B., & Kirişoğlu, S. (2025). Detection of economic crises with language models and comparative analysis of simple time series analysis models and machine learning algorithms on the stock market. IEEE Access, 13, 83255–83274. https://doi.org/10.1109/ACCESS.2025.3567743
Kumar, K. P. M., Dhanasekaran, S., Punithavathi, I. S. H., Duraipandy, P., Dutta, A. K. et al. (2022). Bird Swarm Algorithm with Fuzzy Min-Max Neural Network for Financial Crisis Prediction. Computers, Materials & Continua, 73(1), 1541–1555. https://doi.org/10.32604/cmc.2022.028338
Kyriakou, N., Loukis, E., & Μaragoudakis, M. (2023). Predicting firms’ resilience to economic crisis using artificial intelligence for optimizing economic stimulus programs. Transforming Government: People, Process and Policy, 18(3), 400–416. https://doi.org/10.1108/tg-08-2022-0112
Lezcano Lastra, A., Llamosas García, G., López Cagigas, A., & Parra Rodríguez, F. J. (2023). Predicting the tide of the pandemic: An in-depth analysis of forecasting models for COVID-19 in Cantabria. Boletín de Estadística e Investigación Operativa (BEIO), 39(2), 36–49. https://www.seio.es/beio/predicting-the-tide-of-the-pandemic-an-in-depth-analysis-of-forecasting-models-for-covid-19-in-cantabria/
Li, D. (2023). Research on forecasting model of corporate financial crisis based on pattern recognition and artificial intelligence. Applied Mathematics and Nonlinear Sciences, 8(2), 3553–3566. https://doi.org/10.2478/amns.2021.2.00268
Li, H., Cheng, Z. Y., Fu, X., Liu, M., Liu, P., Cao, W., Liang, Z., Wang, F., & Sun, B. (2024). Decoding acute myocarditis in patients with COVID-19: Early detection through machine learning and hematological indices. Computers in Biology and Medicine, 173, 108097. https://doi.org/10.1016/j.isci.2023.108524
Liu, H., & Shen, Y. (2020). Forecasting carbon price using empirical wavelet transform and GRU neural network. Applied Energy, 262, 114567. https://doi.org/10.1080/17583004.2019.1686930
Liu, X., Zhi, W., & Akhundzada, A. (2025). Enhancing performance prediction of municipal solid waste generation: A strategic management. Frontiers in Environmental Science, 13, 1553121. https://doi.org/10.3389/fenvs.2025.1553121
Manousiadis, C., & Gaki, E. (2023). Prediction models and testing of resilience in regions: Covid19 economic impact in USA counties study case, REGION. Vienna, Austria, 10(1), 113–132. https://doi.org/10.18335/region.v10i1.449
Moraglio, F., & Ragusa, C. (2024). Day-Ahead Electricity Price Forecasting in the Contemporary Italian Market. Energy Economics, 126, 107278. https://doi.org/10.1109/ACCESS.2024.3403422
Mousavimehr, S. M., & Kavianpour, M. R. (2025). A non-stationary downscaling and gap-filling approach for GRACE/GRACE-FO data under climatic and anthropogenic influences. Applied Water Science, 15, 91 (2025). https://doi.org/10.1007/s13201-025-02427-z
Puli, S., Thota, N., & Subrahmanyam, A. (2024). Assessing machine learning techniques for predicting banking crises in India. Journal of Risk and Financial Management, 17(4), 141. https://doi.org/10.3390/jrfm17040141
Ramakrishnaiah, Y., Macesic, N., Webb, G., Peleg, A., & Tyagi, S. (2025). EHR-ML: A data-driven framework for predictive modeling under crisis conditions. International Journal of Medical Informatics, 196, 105816. https://doi.org/10.1016/j.ijmedinf.2025.105816
Reier Forradellas, R. F., & Garay Gallastegui, L. M. (2021). Digital transformation and artificial intelligence applied to business: Legal regulations, economic impact and perspective. Laws, 10(3), 70. https://doi.org/10.3390/laws10030070
Reis, I., Marcondes, R., & Oliveria, S. (2022). On the enrichment of time series with textual data for forecasting agricultural commodity prices. MethodsX, 9, 101758. https://doi.org/10.1016/j.mex.2022.101758
Rodrigues, L., Rodrigues, L., & Bacchi M. R. P. (2022). Performance evaluation of forecasting models based on time series and machine learning techniques: an application to light fuel consumption in Brazil. International Journal of Energy Sector Management, 16(4), 636–658. https://doi.org/10.1108/IJESM-02-2021-0009
Tamasiga, P., Ouassou, E. H., Onyeaka, H., Bakwena, M., Happonen, A., & Molala, M. (2023). Forecasting disruptions in global food value chains to tackle food insecurity: The role of AI and big data analytics – A bibliometric and scientometric analysis. Journal of Agriculture and Food Research, 14, 100819. https://doi.org/10.1016/j.jafr.2023.100819
Wang, X., & Li, M. (2022). Determinants of Regional Economic Resilience to Economic Crisis: Evidence from Chinese Economies. Sustainability, 14(2), 809. https://doi.org/10.3390/su14020809
Wu, J. T.-Y., Armengol de la Hoz, M. Á., Kuo, P.-C., Paguio, J. A., Yao, J. S., Dee, E. C., Yeung, W., Jurado, J., Moulick, A., Milazzo, C., Peinado, P., Villares, P., Cubillo, A., Varona, J. F., Lee, H.-C., Estirado, A., Castellano, J. M., & Celi, L. A. (2022). Developing and validating multi-modal models for mortality prediction in COVID-19 patients: A multi-center retrospective study. Journal of Digital Imaging, 35, 1514–1529. https://doi.org/10.1007/s10278-022-00674-z
Xie, T., Huang, Z., Tan, T., & Chen, Y. (2024). Forecasting China’s agricultural carbon emissions: A comparative study based on deep learning models. Ecological Informatics, 82, 102661. https://doi.org/10.1016/j.ecoinf.2024.102661
Xu, Z., & Shi, J. (2025). Research on the national security risk assessment model: A case study of political security in China. Humanities and Social Sciences Communications, 12(1), 906. https://doi.org/10.1057/s41599-025-05278-w
Yang, D., Tang, Y., Chan, V., Fang, Q., Chan, S., Luo, H., Wong, I., Ou, H., Chan, E., Bishai, D., Chen, Y., Knapp, M., Jit, M., & Craig, D., Li, X. (2025). Population-wide depression incidence forecasting comparing autoregressive integrated moving average and vector autoregressive integrated moving average to temporal fusion transformers: Longitudinal observational study. Journal of Medical Internet Research, 27, e67156. https://doi.org/10.2196/67156
Yu, J., & Chang, K.-C. (2020). Neural network predictive modeling on dynamic portfolio management—A simulation-based portfolio optimization approach. Journal of Risk and Financial Management, 13(11), 285. https://doi.org/10.3390/jrfm13110285
Descargas
Publicado
Número
Sección
Licencia
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
La revista se acoge a la licencia Licencia Atribución (CC BY), permitiendo la posibilidad de copiar, distribuir, exhibir, y producir obras derivadas, siempre y cuando se reconozca y cite al autor.
