Fund2Persona: A Framework for Building and Refining Financial Advisor Personas from Fund Disclosure Data
arXiv:2606.29793v1 Announce Type: new Abstract: Demand for personalized financial advising is growing, but consistent advisor expertise is difficult to obtain, scale, and encode in LLM systems. Simple persona prompts rarely specify how a financial advisor should reason and often drift toward generic recommendations. We propose Fund2Persona, a framework that grounds financial-advisor personas in fund disclosures, holdings transitions, market context, and manager commentary, then refines them through an agentic actor--scorer--patcher loop. We evaluate the resulting personas on held-out holdings-
The increasing sophistication of LLMs and growing demand for personalized financial advice are converging, making automated, expert-level financial reasoning a critical frontier for AI. This paper presents a concrete framework for achieving expert-level reasoning in financial advice and addresses the problem of drift and generic recommendations in LLM systems.
This development indicates a significant step towards autonomous AI agents that can provide nuanced, expert-level financial advice, potentially disrupting traditional advisory services and financial education. It addresses crucial limitations in current LLM applications for complex domain-specific tasks.
The ability to ground AI personas in deep financial data and refine them through an agentic loop changes how financial advice can be scaled and democratized, moving from human-dependent expertise to AI-driven insights. It shifts the paradigm for building robust, specialized financial AI agents.
- · AI developers
- · Robo-advisors
- · Investment platforms
- · Individual investors
- · Human financial advisors (generalists)
- · Traditional wealth management firms
- · Off-the-shelf LLM providers
Financial institutions begin integrating sophisticated AI advisor personas into their service offerings for personalized client engagement.
The cost of high-quality financial advice decreases significantly, making it accessible to a much wider demographic and potentially increasing overall market participation.
AI-driven financial advice could lead to more efficient capital allocation and potentially impact market stability if these systems become widely adopted and interconnected.
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Read at arXiv cs.CL