The Role of Artificial Intelligence in Wealth Advisory: Enhancing Personalized Investment Strategies Through DataDriven Decision Making

Srinivasa Rao Challa

Authors

Srinivasa Rao Challa Sr. Manager, Charles Schwab, Austin, TX, USA. Author

Keywords:

Artificial Intelligence, Wealth Advisory, AI Trading Models, Investment Strategies, Data-Driven Approach, Portfolio Performance, Asset Managers, Wealth Managers, Financial Decisions, AI-Based Techniques, Wealth Management, Investment Solutions, ESG Criteria, Sustainable Finance, Greenwashing, Passive Funds, Personalized Investment Strategy, Managerial Implications, AI Integration, Financial Advisors, ESG Investments

Abstract

This essay concentrates on the role of artificial intelligence (AI) in wealth advisory. In particular, the advent of AI technologies has provided opportunities for customizing investment strategies by taking a data-driven approach. The positive implications of AI trading applications in terms of portfolio performance are very attractive to asset and wealth managers. Under these premises, this paper explores the potential contribution of AI trading models to contemporary investment practices and discusses the relevant managerial implications. Financial decisions are being increasingly influenced by the percolation of AI-based techniques. In the field of wealth management, AI technology is still in the development phase; however, wealth advisors are aware that they have to adapt their perspectives and integrate AI methodologies to offer investment solutions that go beyond traditional and passive funds. The findings of this essay are, by no means, intended as advice, a decision help or support, an endorsement, an investment recommendation, or trading advice. It is always tailored to personal conditions, operating objectives, and other subjective factors that banks, financial advisors, private bankers, asset managers, and wealth professionals can define in response to the changing needs of the market and the regulatory environment. The essay describes an integrated AI-driven trading model that encompasses an effective integration of ESG criteria into the investment choices of a bank. The findings present how market trends about ESG investments can be employed to configure a personalized ESG investment strategy for an investor, thereby countering "greenwashing" and offering an effective scientific approach to sustainable finance that goes one step beyond a traditional passive strategy. These results are relevant for asset and wealth managers to guide the future implementation of AI techniques within portfolio management.

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