Has the World of Finance Changed? A Review of the Influence of Artificial Intelligence on Financial Management Studies
Keywords:
Artificial Intelligence (AI); Financial Management Studies, Financial Data
Abstract
Artificial intelligence (AI) has had a tremendous impact on financial management research, revolutionizing how financial data is analyzed and decisions are made. It has the ability to improve accuracy and efficiency while also lowering costs and providing fresh insights into financial markets and investments. This review will look at some of the important areas of financial management research that AI has influenced. One of the most notable effects of AI on financial management research has been in risk management. AI algorithms, with their ability to analyze vast amounts of data, can detect patterns and anomalies that humans may not notice right away and have had a significant impact on financial forecasting and prediction, generating predictions about future market movements and investment opportunities by analyzing historical data, market trends, and other relevant factors. In conclusion, artificial intelligence (AI) has had a tremendous impact on financial management research, revolutionizing how financial data is analyzed and decisions are made, with significant potential to disrupt traditional financial management techniques. AI has the ability to increase accuracy and efficiency, cut costs, and provide fresh insights into financial markets and investments. As AI technology advances, its impact on financial management studies is likely to rise.
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References
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Soviany, C. (2018). The benefits of using artificial intelligence in payment fraud detection: A case study. Journal of Payments Strategy & Systems, 12(2), 102-110.
Qi, Y. & Xiao, J. (2018). Fintech: AI powers financial services to improve people's lives. Communications of the ACM, 61(11), 65-69.
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Awasthi, S. (2023). The Role of ChatGPT in Enhancing Financial Literacy and Education. Journal of Applied Management-Jidnyasa, 15(1), 13-18.
Awotunde, J. B., Misra, S., Ayeni, F., Maskeliunas, R. & Damasevicius, R. (2022, March). Artificial intelligence-based system for bank loan fraud prediction. In Hybrid Intelligent Systems: 21st International Conference on Hybrid Intelligent Systems (HIS 2021), 463-472. Cham: Springer International Publishing.
Aziz, S. & Dowling, M. (2019). Machine learning and AI for risk management. Disrupting Finance: FinTech and Strategy in the 21st Century, 33-50.
Bao, Y., Hilary, G. & Ke, B. (2022). Artificial intelligence and fraud detection. Innovative Technology at the Interface of Finance and Operations, I, 223-247.
Belanche, D., Casaló, L. V. & Flavián, C. (2019a). Artificial Intelligence in FinTech: Understanding robo-advisors adoption among customers. Industrial Management and Data Systems, 119(7), 1411–1430.
Belanche, D., Casaló, L. V. & Flavián, C. (2019b). Artificial Intelligence in FinTech: understanding robo-advisors adoption among customers. Industrial Management & Data Systems, 119(7), 1411–1430.
Carta, S., Podda, A. S., Reforgiato Recupero, D. & Stanciu, M. M. (2022, February). Explainable AI for financial forecasting. In Machine Learning, Optimization, and Data Science: 7th International Conference, LOD 2021, Grasmere, UK, October 4–8, 2021, Revised Selected Papers, Part II (pp. 51-69). Cham: Springer International Publishing.
Choi, D. & Lee, K. (2018). An artificial intelligence approach to financial fraud detection under IoT environment: A survey and implementation. Security and Communication Networks, 2018.
De Bruyn, A., Viswanathan, V., Beh, Y. S., Brock, J. K. U. & Von Wangenheim, F. (2020). Artificial intelligence and marketing: Pitfalls and opportunities. Journal of Interactive Marketing, 51(1), 91-105.
Han, J., Huang, Y., Liu, S. & Towey, K. (2020). Artificial intelligence for anti-money laundering: a review and extension. Digital Finance, 2(3-4), 211-239.
Jung, D., Dorner, V., Weinhardt, C. & Pusmaz, H. (2018). Designing a robo-advisor for risk-averse, low-budget consumers. Electronic Markets, 28(3), 367–380.
Kalyani, J., Bharathi, P. & Jyothi, P. (2016). Stock trend prediction using news sentiment analysis. ArXiv preprint arXiv:1607.01958.
Kaur, D., Sahdev, S. L., Sharma, D. & Siddiqui, L. (2020). Banking 4.0: ‘The influence of artificial intelligence on the banking industry & how AI is changing the face of modern-day banks. International Journal of Management, 11(6).
Krause, D. (2023). ChatGPT and Other AI Models as a Due Diligence Tool: Benefits and Limitations for Private Firm Investment Analysis. Available at SSRN 4416159.
Lopes, Jorge, and José Luís Pereira. (2019). Blockchain projects ecosystem: A review of current technical and legal challenges. In Advances in Intelligent Systems and Computing. Cham: Springer, 83–92.
Lee, I. & Shin, Y. J. (2020). Machine learning for enterprises: Applications, algorithm selection, and challenges. Business Horizons, 63(2), 157-170.
Mamoshina, P., Lucy, O., Yury, Y., Alex, O., Alex, B., Pavel, P., Eugene, I, Alexander, A., Konstantin, R. and Alexander, Z. (2018). Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare. Oncotarget, 9, 5665–90.
Manser Payne, E. H., Peltier, J. & Barger, V. A. (2021). Enhancing the value co-creation process: artificial intelligence and mobile banking service platforms. Journal of Research in Interactive Marketing, 15(1), 68-85.
Mehta, P., Pandya, S. & Kotecha, K. (2021). Harvesting social media sentiment analysis to enhance stock market prediction using deep learning. PeerJ Computer Science, 7, e476.
Meske, C., Bunde, E., Schneider, J. & Gersch, M. (2022). Explainable artificial intelligence: objectives, stakeholders, and future research opportunities. Information Systems Management, 39(1), 53-63.
Mhlanga, D. (2020). Industry 4.0 in finance: the impact of artificial intelligence (AI) on digital financial inclusion. International Journal of Financial Studies, 8(3), 45.
Mhlanga, D. (2021). Financial inclusion in emerging economies: The application of machine learning and artificial intelligence in credit risk assessment. International Journal of Financial Studies, 9(3), 39.
Mo?teanu, N. R. (2019). International Financial Markets face to face with Artificial Intelligence and Digital Era. Theoretical & Applied Economics, 26(3).
Murugesan, R. & Manohar, V. (2019). AI in Financial Sector–A Driver to Financial Literacy. Shanlax International Journal of Commerce, 7(3), 66-70.
Musto, C., Semeraro, G., Lops, P., De Gemmis, M. & Lekkas, G. (2015). Personalized finance advisory through case-based recommender systems and diversification strategies. Decision Support Systems, 77, 100-111.
Niszczota, P. & Abbas, S. (2023). GPT as a Financial Advisor. Available at SSRN 4384861.
Nkomo, B. K. & Breetzke, T. (2020, March). A conceptual model for the use of artificial intelligence for credit card fraud detection in banks. In 2020 Conference on Information Communications Technology and Society (ICTAS) (pp. 1-6). IEEE.
Pal, A., Indapurkar, K. & Gupta, K. P. (2021). Gamification of financial applications and financial behavior of young investors. Young Consumers, 22(3), 503-519.
Paul, S. (2019). Use of Blockchain and Artificial Intelligence to Promote Financial Inclusion in India Smita Miglani Indian Council for Research on International Economic Relations.
Rathore, B. (2023). Future of AI & Generation Alpha: ChatGPT beyond Boundaries. Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal, 12(1), 63-68.
Rigano, C. (2019). Using artificial intelligence to address criminal justice needs. National Institute of Justice Journal, 280(1-10), 17.
Rodrigues, L. F., Oliveira, A., Costa, C. J. & Rodrigues, H. (2018). Gamification to teach and assess financial education: a case study of self-directed bank investors. Gamification to teach and assess financial education: a case study of self-directed bank investors, 1851-1882.
Rodriguez-Ruiz, J., Alvarez-Delgado, A. & Caratozzolo, P. (2021, December). Use of Natural Language Processing (NLP) Tools to Assess Digital Literacy Skills. In 2021 Machine Learning-Driven Digital Technologies for Educational Innovation Workshop (pp. 1-8). IEEE.
Roh, T., Park, B. & Xiao, S. (2023). Adoption of AI-enabled robo-advisors in Fintech: Simultaneous employment of UTAUT and the Theory of Reasoned Action. Journal of Electronic Commerce Research, 24(1), 29–47.
Ryman-Tubb, N. F., Krause, P. & Garn, W. (2018). How Artificial Intelligence and machine learning research impacts payment card fraud detection: A survey and industry benchmark. Engineering Applications of Artificial Intelligence, 76, 130-157.
Seemakurthi, P., Zhang, S. & Qi, Y. (2015, April). Detection of fraudulent financial reports with machine learning techniques. In 2015 Systems and Information Engineering Design Symposium (pp. 358-361). IEEE.
Shah, D., Isah, H. & Zulkernine, F. (2019). Stock market analysis: A review and taxonomy of prediction techniques. International Journal of Financial Studies, 7(2), 26.
Sharifani, K. & Amini, M. (2023). Machine Learning and Deep Learning: A Review of Methods and Applications. World Information Technology and Engineering Journal, 10(07), 3897-3904.
Soni, V. D. (2019). Role of artificial intelligence in combating cyber threats in banking. International Engineering Journal for Research & Development, 4(1), 7-7.
Schemmel, J. (2020). Artificial Intelligence and the Financial Markets: Business as Usual? Regulating Artificial Intelligence, 255-276.
Silva, R. D. (2021). Calls for behavioral biometrics as bank fraud soars. Biometric Technology Today, 2021(9), 7-9.
Singh, G., Garg, V. & Tiwari, P. (2020). Application of Artificial Intelligence on Behavioral Finance. In Recent Advances in Intelligent Information Systems and Applied Mathematics (pp. 342-353). Springer International Publishing.
Singh, H., Singh, A. & Nagpal, E. (2022). Demystifying behavioral biases of traders using machine learning. Decision Intelligence Analytics and the Implementation of Strategic Business Management, 179-188.
Shanmuganathan, M. (2020). Behavioral finance in an era of artificial intelligence: Longitudinal case study of robo-advisors in investment decisions. Journal of Behavioral and Experimental Finance, 27, 100297.
Soviany, C. (2018). The benefits of using artificial intelligence in payment fraud detection: A case study. Journal of Payments Strategy & Systems, 12(2), 102-110.
Qi, Y. & Xiao, J. (2018). Fintech: AI powers financial services to improve people's lives. Communications of the ACM, 61(11), 65-69.
Xie, M. (2019, April). Development of artificial intelligence and effects on the financial system. In Journal of Physics: Conference Series, 1187(3), 032084. IOP Publishing.
Yaacoub, J. P. A., Salman, O., Noura, H. N., Kaaniche, N., Chehab, A. & Malli, M. (2020). Cyber-physical systems security: Limitations, issues and future trends. Microprocessors and Microsystems, 77, 103201.
Yue, T., Au, D., Au, C. C. & Iu, K. Y. (2023). Democratizing financial knowledge with ChatGPT by OpenAI: Unleashing the Power of Technology. Available at SSRN 4346152.
Zaremba, A. & Demir, E. (2023). ChatGPT: Unlocking the Future of NLP in Finance. Available at SSRN 4323643.
Published
2023-11-12
How to Cite
Zakaria, S., Abdul Manaf, S. M., Amron, M. T., & Mohd Suffian, M. T. (2023). Has the World of Finance Changed? A Review of the Influence of Artificial Intelligence on Financial Management Studies. Information Management and Business Review, 15(4(SI)I), 420-432. https://doi.org/10.22610/imbr.v15i4(SI)I.3617
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Research Paper
Copyright (c) 2023 Shahsuzan Zakaria, Suhaily Maizan Abdul Manaf, Mohd Talmizie Amron, Mohd Taufik Mohd Suffian
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