The Advent of Generative AI and Financial Industry
DOI:
https://doi.org/10.47941/ijf.2210Keywords:
Generative AI, Machine Learning, Deep Learning, Financial InstitutionsAbstract
Purpose: This paper explores the recent literature on Generative AI applications in the financial industry and delineates its role in the future.
Methodology: Our paper follows secondary research analyzing current literature on Generative AI in finance. It is one of the essential tools for understanding background information, identifying research problems, and filling the literature gaps. This paper studies how Generative AI has potential financial benefits and risks, providing unique insights into the financial landscape in the coming years.
Findings: The findings unveil that Generative AI can become a strategic tool to redefine financial services and operational effectiveness. It can substantially improve the services by reducing costs, bringing efficiency, and enhancing corporate performance. It has the enormous transformative power to revolutionize client product and service offerings, improving risk management assessments and bringing efficiency to operations. However, our study indicates that the financial service industry can get into practices and decisions that are potentially unethical and financial exclusion due to an embedded bias in its algorithm and design of Generative AI technologies. Since Generative AI continues to evolve, its role and effectiveness in decision-making are expected to shape the financial services landscape significantly.
Unique Contribution to Theory, Practice, and Policy: Generative AI can be a game changer for the financial industry, fueling digital transformation across industries. The transformative potential of generative AI can optimize operations, revolutionize customer experiences, and drive innovation seamlessly in finance. Our paper suggests how policymakers can foresee the challenges ahead due to the Generative AI in finance services, which is challenging the existing regulatory landscape. To stay ahead in the competition, financial firms must balance data privacy and algorithmic bias and ensure the responsible use of AI.
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References
Artificial Intelligence Index Report 2024 https://aiindex.stanford.edu/report/
Bai, X., Zhuang, S., Xie, H., and Gua, L (2024). Leveraging Generative Artificial Intelligence for Financial Market Trading Data Management and Prediction, 2024 doi: 10.20944/preprints 202407.0084.v1
Bianchi, D., Andrea. D., and Ivan. P. (2022). Taming Momentum Crashes (August 4, 2022). Available at SSRN: https://ssrn.com/abstract=4182040 or http://dx.doi.org/10.2139/ssrn.4182040
Chen, L., Pelger, M., and Zhu, J. (2023). Deep Learning in Asset Pricing. Management Science. 70. 10.1287/mnsc.2023.4695.
David E Rapach, Jack K Strauss, and Guofu Zhou. International stock return predictability: what is the role of the United States? The Journal of Finance, 68(4):1633{1662, 2013.
Dong, Y., and Gao, C. (2021). An adaptive dimension reduction algorithm for latent variables of variational autoencoder. CoRR, 2021.
Gandhmal, D. P., and Kumar, K. (2019). Systematic analysis and review of stock market prediction techniques. Computer Science Review, 34, 100190. https://doi.org/10.1016/j.cosrev.2019.08.001
Goldman Sachs. 2023. Generative AI could raise global GDP by 7 percentage. https://www.goldmansachs.com/intelligence/pages/generative-ai-could-raise-global-gdp-by-7-percent.html
Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep learning, MIT Press (Chapter 15).
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. and Bengio, Y., Generative adversarial nets. In Advances in Neural Information Processing Systems, Vol. 27, 2014.
Gu, S., Kelly, B., and Xiu, D. (2021). Autoencoder asset pricing models, Journal of Econometrics, Volume 222, Issue 1, Part B, May 2021, Pages 429-450.
Guresen, E., Kayakutlu, G. and Daim, T.U. (2011), Using artificial neural network models in stock market index prediction. Expert Syst. Appl., 2011, 38(8), 10389–10397.
Harvey, C. R. and Liu, Y. (2016). Lucky factors. Technical report, Duke University, 2016.
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory, Neural Computation 9(8) (1997) 1735-1780.
Jiang, W., Yang, T., Li, A., Lin, Y., and Bai, X. (2024). The Application of Generative Artificial Intelligence in Virtual Financial Advisor and Capital Market Analysis. Academic Journal of Sociology and Management, 2(3), 40-46.
Khalil, F. and G. Pipa, “Is Deep-Learning and Natural Language Processing Transcending the Financial Forecasting? Investigation Through Lens of News Analytic Process,” Computational Economics 60 (1): 147–71, 2022, https://doi.org/10.1007/s10614-021-10145-2.
Kozak, S., Nagel, S., and Santosh, S. (2019). Shrinking the cross section. Journal of Financial Economics, 2019.
Kumar, R., Kumar, P., and Kumar, Y. (2021). Analysis of financial time series forecasting using deep learning model. In 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence). (pp. 877–881). IEEE.
Lara-Benítez, P., Carranza-García, M., and Riquelme, J. C. (2021). An experimental review on deep learning architectures for time series forecasting. International Journal of Neural Systems, 31(3), 2130001. https://doi.org/10.1142/S0129065721300011
Liu, B., Cai, G., Ling, Z., Qian, J. and Zhang, Q (2024). Precise positioning and prediction system for autonomous driving based on generative artificial intelligence. Applied Computer. Engineering 2024, 64, 36–43, https://doi.org/10.54254/2755-2721/64/20241349.
Liu, Z., Du, G., Zhou, S., Lu, H., and H. Ji, “Analysis of Internet Financial Risks Based on Deep Learning and BP Neural Network,” Computational Economics 59 (4): https://doi.org/10.1007/s10614-021-10229-z. 1481–99, 2022.
Lu, W., Li, J., Li, Y., Sun, A. J., and Wang, J.Y. (2020). A CNN-LSTM-based model to forecast stock prices, Complexity 2020 6622927.
Mancisidor, R., Kampffmeyer, M., Aas, K., and Jenssen, R (2021). Learning latent representations of bank customers with the Variational Autoencoder, Expert Systems with Applications, Vol 164, 2021, 114020.
McKinsey 2023, The economic potential of generative AI: The next productivity frontier,
MIT Technology Review Insights 2023. Finding value in generative AI for financial services, 2023
Nosratabadi, S., Mosavi, A., Duan, P., Ghamisi, P., Filip, F., Band, S. S., Reuter, U., Gama, J., and Gandomi, A. H. (2020). Data science in economics: Comprehensive review of advanced machine learning and deep learning methods. Mathematics, 8(10), 1799. https://doi.org/103390/math8101799
Qian, K., Fan, C., Li, Z., Zhou, H., and Ding, W. (2024). Implementation of Artificial Intelligence in Investment Decision-making in the Chinese A-share Market. Journal of Economic Theory and Business Management, 1(2), 36-42.
Selvin, S., Vinayakumar, R., Gopalakrishnan, E.A., Menon, V. K., and Soman, K. P. (2017). Stock price prediction using LSTM, RNN and CNN-sliding window model, in: Proc. of the 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017.
Sezer, O. B., Gudelek, M. U., and Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning: A systematic literature review:2005–2019. Applied Soft Computing, 90, 106181. https://doi.org/10.1016/j.asoc.2020.106181
Sha, X. (2024). Time Series Stock Price Forecasting Based on Genetic Algorithm (GA)-Long Short-Term Memory Network (LSTM) Optimization. arXiv preprint arXiv:2405.03151.
Shabsigh, G. and Boukherouaa, E.B. (2023). Generative Artificial Intelligence in Finance: Risk Considerations, International Monetary Fund, FinTech Notes 2023.
Shihao Gu, Bryan Kelly, and Dacheng Xiu. Empirical asset pricing via machine learning. Review of Financial Studies, 2019.
Sirignano, J.A. (2019). Deep learning for limit order books. Quantitative Finance, 2019, 19(4), 549–570.
Tian, J., Qi, Y., Li, H., Feng, Y., and Wang, X. (2024). Deep Learning Algorithms Based on Computer Vision Technology and Large-Scale Image Data. Journal of Computer Technology and Applied Mathematics,1(1), 109-115.
Vuletić, M., Prenzel, F., and Cucuringu, M. (2024). Fin-GAN: forecasting and classifying financial time series via generative adversarial networks, Quantitative Finance, 24:2, 175-199, DOI: 10.1080/14697688.2023.2299466.
Wang, Y., Zhan, X., Zhan, T., Xu, J., & Bai, X. (2024). Machine Learning-Based Facial Recognition for Financial Fraud Prevention. Journal of Computer Technology and Applied Mathematics, 1(1), 77–84. https://doi.org/10.5281/zenodo.11004115
Workday's 2023 Survey Report, Global CFO AI Indicator Report https://forms.workday.com/content/dam/web/sg/documents/reports/cfo-global-ai-indicator-report-en-SG.pdf
Zhang, Z., Zohren, S., and Roberts, S. (2019). DeepLOB: Deep convolutional neural networks for limit order books. IEEE Transactions on Signal Processing, 67(11), 3001–3012. https://doi.org/10.1109/TSP.2019.2907260
Zhou, X., Pan, Z., Hu, G., Tang, S. Q., and Zhao, C. (2018). Stock market prediction on high-frequency data using generative adversarial nets, Mathematical Problems in Engineering 2018 (2018) 4907423.
Zhou, Y., Zhan, T., Wu, Y., Song, B., and Shi, C. (2024). RNA secondary structure prediction using transformer-based deep learning models. Applied Computer. Engineering 2024, 64, 95–101, https://doi.org/10.54254/2755- 2721/64/20241362.
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