Research on the Application of Robo-Advisory Systems in Personal Wealth Management: A Case Study of Middle-Class Households in the United States
DOI:
https://doi.org/10.71222/hpngxr49Keywords:
robo-advisors, middle-class households, AI and NLP, Open Banking, financial inclusionAbstract
With the rapid development of artificial intelligence and big data technologies, robo-advisory systems have become an increasingly popular solution for personal wealth management, especially among middle-class households in the United States. This study combines questionnaire-based estimates, platform case analysis, and literature synthesis to examine how robo-advisors support asset allocation, risk management, and goal-based financial planning for middle-income families. Findings indicate that while robo-advisors offer clear benefits in terms of automation, cost efficiency, and improved investment discipline, trust issues, limited algorithmic transparency, and gaps in financial literacy remain key barriers to wider adoption. The paper further identifies emerging technological trends—such as AI integration with natural language processing and Open Banking—and proposes policy recommendations to enhance algorithm accountability, expand financial education, and promote inclusive access. By addressing these challenges and leveraging technological innovations, robo-advisory systems have the potential to democratize professional wealth management and strengthen the financial security of middle-class households.
References
1. J. Wang and P. Wang, "Research on the path of enterprise strategic transformation under the background of enterprise reform," Mod. Econ. Manag. Forum, vol. 6, no. 3, pp. 462-464, 2025, doi: 10.32629/memf.v6i3.4035.
2. K. F. Phoon and F. Koh, “Robo-advisors and wealth management,” J. Financ. Perspect., vol. 4, no. 3, pp. 44–50, 2017, doi: 10.3905/jai.2018.20.3.079.
3. F. Gao, “The Role of Data Analytics in Enhancing Digital Platform User Engagement and Retention,” J. Media J. Commun. Stud., vol. 1, no. 1, pp. 10–17, Apr. 2025, doi: 10.71222/z27xzp64.
4. F. D’Acunto, N. Prabhala, and A. G. Rossi, “The promises and pitfalls of robo-advising,” Rev. Financ. Stud., vol. 32, no. 5, pp. 1983–2020, 2019, doi: 10.1093/rfs/hhz014.
5. J. Jagtiani and C. Lemieux, “The roles of alternative data and machine learning in fintech lending: Evidence from the LendingClub consumer platform,” Financ. Manag., vol. 48, no. 4, pp. 1009–1029, 2018, doi: 10.1111/fima.12295.
6. L. Yun, “Analyzing Credit Risk Management in the Digital Age: Challenges and Solutions,” Econ. Manag. Innov., vol. 2, no. 2, pp. 81–92, Apr. 2025, doi: 10.71222/ps8sw070.
7. S. Yang, “The Impact of Continuous Integration and Continuous Delivery on Software Development Efficiency,” J. Comput. Signal Syst. Res., vol. 2, no. 3, pp. 59–68, Apr. 2025, doi: 10.71222/pzvfqm21.
8. D. Belanche, L. V. Casaló, and C. Flavián, “Artificial Intelligence in FinTech: Understanding robo-advisors adoption among users,” Ind. Manag. Data Syst., vol. 119, no. 7, pp. 1411–1430, 2019, doi: 10.1108/IMDS-08-2018-0368.
9. A. Bhatia, A. Chandani, R. Atiq, M. Mehta, and R. Divekar, “Artificial intelligence in financial services: A qualitative research to discover robo-advisory services,” Qual. Res. Financ. Mark., vol. 13, no. 5, pp. 632–654, 2021, doi: 10.1108/QRFM-10-2020-0199.
10. A. Lusardi and O. S. Mitchell, “The economic importance of financial literacy: Theory and evidence,” J. Econ. Lit., vol. 52, no. 1, pp. 5–44, 2014, doi: 10.1257/jel.52.1.5.
11. A. Lusardi, “Financial literacy and the need for financial education: Evidence and implications,” Swiss J. Econ. Stat., vol. 155, no. 1, pp. 1–8, 2019, doi: 10.1186/s41937-019-0027-5.
12. M. Reher and S. Sokolinski, “Automation and inequality in wealth management,” Jindal Sch. Manag., 2021.
13. M. Reher, “Robo advisors and access to wealth management,” J. Financ. Econ., vol. 143, no. 2, pp. 431–450, 2024, doi: 10.1016/j.jfineco.2024.103829.
14. D. W. Arner, J. Barberis, and R. P. Buckey, “FinTech, RegTech, and the reconceptualization of financial regulation,” Northwest. J. Int. Law Bus., vol. 37, pp. 371, 2016.
15. T. Hasan and N. Jahan, “Artificial Intelligence and Ethical Challenges in Financial Services: A Framework for Responsible Data Governance and Algorithmic Transparency,” Int. J. Data Sci. Big Data Anal. Predict. Model., vol. 14, no. 8, pp. 16–36, 2024.
