The Augmented Advisor Model in Wealth Management: A Qualitative Exploration of GenAI Application Efficiency and Human Trust Boundaries
DOI:
https://doi.org/10.54097/6aw0jq57Keywords:
Generative Artificial Intelligence, Augmented Advisor, Wealth Management, Human–AI Trust, Human-in-the-LoopAbstract
In the future, the Wealth Management sector may have an upgraded form known as "enhanced advisors". It is expected that generative AI will assist people's work with reasoning abilities; however, they will not fully replace human labour. This paper qualitatively explores the new form through two inter-related aspects: application efficiency and the limits of people's trust. Based on relevant academic literature and institutional reports published from 2019 to 2026, this paper examines how large language models (LLMs) and agentic AI can improve operational efficiency in three areas: back-office compliance, front-end personalisation and client communication workflows; then analyzes unevenly distributed effects using the "prompt dividend" mechanism and supply-side model bias. On this basis, a triangular advisor-client-AI trust mechanism is used to model the generation and maturation process of trust from "avoidance" to "acceptance". Through analysis, although the number of GenAIs has increased through certain means, building trust requires explanation, human management, and organizational supervision. This paper proposes that a zero-trust governance architecture should be integrated with a human-in-the-loop mechanism to address the disconnect between efficiency potential and trust realisation.
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