Kunnathuvalappil Hariharan, Naveen (2018): Artificial Intelligence and human collaboration in financial planning. Published in: Journal of Emerging Technologies and Innovative Research (JETIR) , Vol. 5, No. 7 (July 2018): pp. 1348-1355.
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Abstract
Artificial intelligence (AI) can assist business leaders in automating time-consuming and labor-intensive operations such as data collection, aggregation, and purification. This enables them to devote more time to high-value tasks and make more informed business decisions. While AI has been shown to offer a number of advantages when it comes to data analysis and delivering insight for investment plan creation, it lacks the emotional intelligence required to meet more complicated investing needs. The privacy and security of clients is another issue that has developed as a result of the usage of artificial intelligence in financial planning.Because AI is still a relatively new breakthrough in an industry with millions of dollars invested, some industry insiders are concerned that cyber-security may not be as advanced as the technology itself, putting the company at risk of hacking. This research outlined several advantages that AI offer during financial planning, followed by limitations and challenges that come in decision making process with AI. Last, this article discusses how humans and AI might cooperate to provide a more strategic and realistic perspective when corporate decision-making processes are complex, unpredictable, and ambiguous.
Item Type: | MPRA Paper |
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Original Title: | Artificial Intelligence and human collaboration in financial planning |
Language: | English |
Keywords: | AI; Planning; unpredictability; complexity; ambiguity |
Subjects: | J - Labor and Demographic Economics > J0 - General J - Labor and Demographic Economics > J0 - General > J00 - General J - Labor and Demographic Economics > J4 - Particular Labor Markets |
Item ID: | 109515 |
Depositing User: | Naveen Kunnathuvalappil Hariharan |
Date Deposited: | 02 Sep 2021 11:47 |
Last Modified: | 02 Sep 2021 11:47 |
References: | Beck, Megan, and Barry Libert. 2017. “The Rise of AI Makes Emotional Intelligence More Important.” Harvard Business Review 15. https://www.aimatters.com/s/HBR-The-rise-of-AI-makes-emotional-intelligence-more-improtant.PDF. Bolton, C., V. Machová, and M. Kovacova. 2018. "The Power of Human-machine Collaboration: Artificial Intelligence, Business Automation, and the Smart Economy." Asia-Pacific Financial Markets. https://www.ceeol.com/search/article-detail?id=728359. Boutilier, Craig. 2000. “Decision Making under Uncertainty: Operations Research Meets AI (again).” In AAAI/IAAI, 1145–50. aaai.org. Bughin, Jacques, Michael Chui, and B. McCarthey. 2017. “How to Make AI Work for Your Business.” Harvard Business Review, August. https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Analytics/Our%20Insights/How%20to%20make%20AI%20work%20for%20your%20business/How-to-make-AI-work-for-your-business.pdf. Denby Brandon, E., Jr, and H. Oliver Welch. 2009. The History of Financial Planning: The Transformation of Financial Services. John Wiley & Sons. Doumpos, Michael, and Evangelos Grigoroudis. 2013. Multicriteria Decision Aid and Artificial Intelligence: Links, Theory and Applications. John Wiley & Sons. Greenwood, Robert P. 2002. Handbook of Financial Planning and Control. Gower Publishing, Ltd. Kent Baker, H., and Victor Ricciardi. 2014. Investor Behavior: The Psychology of Financial Planning and Investing. John Wiley & Sons. Kingdon, Jason. 2012. Intelligent Systems and Financial Forecasting. Springer Science & Business Media. Phillips-Wren, Gloria, and Nikhil Ichalkaranje. 2008. Intelligent Decision Making: An AI-Based Approach. Springer. Schuller, Dagmar, and Björn W. Schuller. 2018. “The Age of Artificial Emotional Intelligence.” Computer 51 (9): 38–46. Simões-Marques, Mário, and José R. Figueira. 2019. “How Can AI Help Reduce the Burden of Disaster Management Decision-Making?” In Advances in Human Factors and Systems Interaction, 122–33. Springer International Publishing. Stacey, Martin, P. John Clarkson, and Claudia Eckert. 2000. “Signposting: An AI Approach to Supporting Human Decision Making in Design.” In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 35111:141–50. American Society of Mechanical Engineers. Subramanian, Ramesh. 2017. “Emergent AI, Social Robots and the Law: Security, Privacy and Policy Issues.” AI, Social Robots and the Law: Security, Privacy …. https://papers.ssrn.com/abstract=3279236. Sucar, and L. Enrique. 2011. Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions: Concepts and Solutions. IGI Global. Torra, Vicenç, Yasuo Narukawa, and Yasunori Endo. 2014. Modeling Decisions for Artificial Intelligence: 11th International Conference, MDAI 2014, Tokyo, Japan, October 29-31, 2014, Proceedings. Springer International Publishing. Tzafestas, S. G., and H. B. Verbruggen. 2012. Artificial Intelligence in Industrial Decision Making, Control and Automation. Springer Netherlands. Wong, W. K., Z. X. Guo, and S. Y. S. Leung. 2013. Optimizing Decision Making in the Apparel Supply Chain Using Artificial Intelligence (AI): From Production to Retail. Elsevier. Xiao, Liang, Xiaoyue Wan, Xiaozhen Lu, Yanyong Zhang, and Di Wu. 2018. “IoT Security Techniques Based on Machine Learning: How Do IoT Devices Use AI to Enhance Security?” IEEE Signal Processing Magazine 35 (5): 41–49. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/109515 |