Artificial Intelligence in Investment Decision-Making:Opportunities, Risks, and Human OversightIn Financial Institutions
DOI:
https://doi.org/10.14419/s894r354Keywords:
Artificial Intelligence; Investment Decision-Making; Financial Institutions; Machine Learning; Predictive Analytics; Risk ManagementAbstract
The accelerating advancement of Artificial Intelligence (AI) is reshaping investment decision-making across financial institutions. This article examines the dual role of AI as both an enabler of opportunities and a source of new risks in asset management, portfolio construction, and risk evaluation. By applying techniques such as machine learning, natural language processing, and predictive analytics, firms are able to extract value from extensive datasets, identify emerging market signals, and execute strategies with greater precision. These capabilities promise improved efficiency, competitive advantages, and potentially higher returns. However, their adoption also raises pressing challenges, including algorithmic bias, vulnerabilities to cyberattacks, regulatory ambiguities, and excessive reliance on automated systems. The study emphasizes the need for balancing human expertise with AI-driven insights, ensuring interpretability, accountability, and ethical use. Drawing on empirical studies and industry cases, the paper concludes that AI is not a replacement for human judgment but a complementary tool that requires strong oversight, robust governance, and evolving regulatory frameworks to unlock its full potential.
References
Agarwal, R., Gans, J. S., & Goldfarb, A. (2021). The economics of artificial intelligence: Implications for the future of work and finance. MIT Press.
Baker, M. (2019). The rise of robo-advisors: Automating financial advice. Journal of Financial Innovation, 6(2), 112–129.
Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1–8. https://doi.org/10.1016/j.jocs.2010.12.007.
Bontemps, C., Hilgert, N., & Gourieroux, C. (2021). Predictive analytics in finance: Big data and machine learning. European Journal of Operation-al Research, 292(3), 837–850. https://doi.org/10.1016/j.ejor.2020.11.023.
Charles Schwab. (2019). The digital investor: How technology is transforming retail investment behavior. Charles Schwab & Co.
Chaboud, A. P., Chiquoine, B., Hjalmarsson, E., & Vega, C. (2014). Rise of the machines: Algorithmic trading in the foreign exchange market. The Journal of Finance, 69(5), 2045–2084. https://doi.org/10.1111/jofi.12186.
Chandra, R., He, Y., & Wang, L. (2020). Machine learning applications in financial time series forecasting: A review. Applied Soft Computing, 91, 106–117. https://doi.org/10.1016/j.asoc.2020.106164.
Chukwunweike, C. C., Sharma, D., & Patel, N. (2024). Predictive analytics in financial forecasting: Integrating deep learning and macroeconomic indicators. International Journal of Financial Studies, 12(1), 22–36. https://doi.org/10.3390/ijfs12010022.
Cohen, J., & Hu, T. (2020). Artificial intelligence in financial risk management: Modeling systemic vulnerabilities. Risk Management Journal, 18(4), 215–233.
Dixon, M. F., Klabjan, D., & Bang, J. H. (2020). Machine learning in finance: From theory to practice. Springer. https://doi.org/10.1007/978-3-030-41068-1.
Fang, F. (2021). Alternative data and hedge fund performance: Evidence from satellite imagery and transaction analytics. Journal of Asset Man-agement, 22(3), 178–193.
Feng, G., He, J., & Polson, N. (2020). Deep learning in finance: Sentiment analysis and asset pricing. Financial Innovation, 6(1), 8–22.
Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Opera-tional Research, 270(2), 654–669. https://doi.org/10.1016/j.ejor.2017.11.054.
He, K., Larkin, M., & Zhu, J. (2020). Machine learning for investment management: Current applications and future opportunities. Journal of Fi-nancial Data Science, 2(4), 45–63. https://doi.org/10.3905/jfds.2020.1.039.
Hendershott, T., Jones, C. M., & Menkveld, A. J. (2011). Does algorithmic trading improve liquidity? The Journal of Finance, 66(1), 1–33. https://doi.org/10.1111/j.1540-6261.2010.01624.x.
uang, D., Kou, G., & Peng, Y. (2020). Predictive modeling and analytics in finance: Integrating textual sentiment with quantitative data. Expert Systems with Applications, 150, 113–227. https://doi.org/10.1016/j.eswa.2020.113227.
Kearns, M., & Nevmyvaka, Y. (2019). Machine learning for market microstructure and high-frequency trading. Cambridge University Press.
Michaud, R. (2020). AI-based stress testing and the evolution of hedge fund risk management. Financial Analysts Journal, 76(4), 89–103.
Müller, J., Leippold, M., & Nucera, F. (2020). Machine learning for risk management: Integrating predictive modeling with financial supervision. Journal of Banking & Finance, 118, 105869. https://doi.org/10.1016/j.jbankfin.2020.105869.
Downloads
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal''s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).