INTEGRATING AI –DRIVEN ECONOMIC FORECASTING IN CORPORATE FINANCIAL DECISIONS: A DECISION THEORY PERSPECTIVE
DOI:
https://doi.org/10.51903/jiab.v6i1.989Keywords:
Artifical intelligence, economic forecasting, corporate financial decision, financial strategyAbstract
Global economic uncertainty has pushed companies to make financial decisions that are faster, more accurate, and adaptable. However, the use of Artificial Intelligence (AI) to predict economic conditions in real-time and support corporate financial decisions is still limited-especially in developing countries. This study aims to design an AI-based model capable of forecasting global economic fluctuations and analyzing its impact on corporate financial strategy. A qualitative approach was applied, using case studies and descriptive-exploratory analysis based on secondary data and relevant AI system documentation. The results show that the AI model can identify key economic indicators and provide predictions that enhance financial planning accuracy. Moreover, the model strengthens decision-making by combining data-driven strategies with managerial intuition. This research contributes both theoretically and practically to the use of AI in supporting financial decision-making, particularly in volatile economic environments.
References
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Al-Hasan, A., & Khan, S. A. (2022). The role of artificial intelligence in enhancing financial resilience under macroeconomic volatility. Journal of Financial Innovation, 6(2), 87–104. https://doi.org/10.1007/s42521-022-00067-5
Baek, Y., & Kim, H. Y. (2019). Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data. PLOS ONE, 14(2), e0212320. https://doi.org/10.1371/journal.pone.0212320
Baek, Y., & Kim, H. Y. (2020). Modelling and forecasting stock market volatility using hybrid LSTM-CNN models. Applied Soft Computing, 94, 106452. https://doi.org/10.1016/j.asoc.2020.106452
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188. https://doi.org/10.2307/41703503
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654–669. https://doi.org/10.1016/j.ejor.2017.11.054
Ghosh, R., & Dey, L. (2021). Sentiment analysis and deep learning for financial market prediction. International Journal of Forecasting, 37(2), 777–797. https://doi.org/10.1016/j.ijforecast.2020.05.005
Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049–1064. https://doi.org/10.1016/j.im.2016.07.004
Horngren, C. T., Datar, S. M., & Rajan, M. V. (2012). Cost accounting: A managerial emphasis (14th ed.). Pearson Education.
Jha, S., & Bose, I. (2021). AI and analytics in corporate finance: A framework for strategic decision-making. Journal of Business Research, 132, 635–645. https://doi.org/10.1016/j.jbusres.2020.10.061
Kirkos, E., Spathis, C., & Manolopoulos, Y. (2007). Data mining techniques for financial fraud detection. Expert Systems with Applications, 32(4), 995–1003. https://doi.org/10.1016/j.eswa.2006.02.009
Li, X., Xie, H., Chen, L., Wang, J., & Deng, X. (2020). News impact on stock market prediction based on deep learning models. Neural Computing and Applications, 32(3), 625–638. https://doi.org/10.1007/s00521-018-3911-9
Nguyen, T., & Nguyen, T. H. (2023). Deep learning and AI adoption in financial decision-making: Evidence from emerging markets. Technological Forecasting and Social Change, 191, 122556. https://doi.org/10.1016/j.techfore.2023.122556
Park, S., & Lee, J. (2021). Organizational readiness for AI: A multi-level analysis in financial services. Information & Management, 58(4), 103458. https://doi.org/10.1016/j.im.2021.103458
Romney, M. B., & Steinbart, P. J. (2018). Accounting information systems (14th ed.). Pearson.
Simon, H. A. (1979). Rational decision making in business organizations. The American Economic Review, 69(4), 493–513.
Sundararajan, A., & Ray, S. (2022). AI and financial forecasting: A systematic literature review. Journal of Financial Technology, 4(2), 45–63.
Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.
Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for financial decision making. Decision Support Systems, 81, 67–78. https://doi.org/10.1016/j.dss.2015.12.006
Youssef, M., & Awad, M. (2022). Real-time economic forecasting using AI-enhanced macroeconomic models. Economic Modelling, 112, 105849. https://doi.org/10.1016/j.econmod.2022.105849
Zhang, G. P., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14(1), 35–62. https://doi.org/10.1016/S0169-2070(97)00044-7
