User Behavior Analysis in AI-Powered Financial Advisory Platforms

Padma naresh Vardhineedi¹ & Dr. Pooja Sharma²

¹University of Missouri

Kansas City, 5000 Holmes St, Kansas City, MO 64110, US

²IIMT University
Pooja512005@Gmail.com
orcid id – 0000-0003-4432-726X

Abstract– The integration of Artificial Intelligence (AI) in financial advisory platforms has profoundly altered the financial services sector, providing users with personalized advice and predictive analytics. Yet, amidst the technological advancements, the research gap in understanding user behavior in relation to AI-driven systems remains a vital challenge. This paper seeks to investigate the research gap in the area of how AI platforms engage with users, affect their decision-making, and drive user engagement. While existing research has revolved around AI’s technical potential, few have considered the psychological, cultural, and behavioral parameters that define user interaction with these platforms. Initial research called attention to issues like trust, transparency, and user adoption, particularly in the realm of algorithmic decision-making. Yet, recent research has shifted towards personalizing financial advice through the consideration of user preference, emotional states, and financial literacy. The expanding use of predictive analytics, behavioral nudging, and machine learning algorithms has introduced new dimensions to drive user engagement. Yet, the call for further research into the ethical implications, transparency of AI systems, and cross-cultural adaptability persists. This paper identifies essential research gaps, specifically in identifying how AI can be optimized to cater to the varied needs of users, ranging from millennials, high-net-worth individuals, and those with different levels of financial literacy. It also touches on the influence of explainable AI and user-centric design on trust and retention. Through a consideration of these aspects, this paper contributes to a more sophisticated understanding of how AI can align more intimately with user behavior to enhance financial decision-making and outcomes.

Keywords– AI-powered financial advisory, user behavior analysis, personalized financial advice, machine learning, trust in AI, predictive analytics, financial literacy, user engagement, algorithmic decision-making, explainable AI, behavioral nudging, ethical considerations, user-centric design, cross-cultural adaptability.

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