FinGPT
As a Product Designer, I collaborated with a stealth fintech startup to design a framework for designing LLM-powered learning experiences in personal finance, where users remain in control, trust is built transparently, and AI is used to clarify, not convince.
The context
Young adults are eager to invest, but few feel equipped to start. In partnership with a stealth fintech startup, I led the design of a framework for using Large Language Models (LLMs) as trusted copilots for personal finance.
This project resulted in a scalable, low-risk, and explainable framework that banks and fintechs could adopt to enhance user learning, not decision-making.
Looking for LLM use cases in personal finance
We set out to answer a foundational question: Where can LLMs responsibly add value in personal finance?
Where can LLMs responsibly add value in personal finance?
Use cases with the following selection criteria:
Use cases with the following elimination criteria:
Identified use case: helping young professionals learn about personal finance using LLMs as an exploration tool
Overview
Role
Product Design Consultant
Impact
This framework was adopted for continued internal development by the client team
Duration
2 months
Team
Product, Engineering
Scope
0 → 1
LLM UX
Product Strategy
Responsible AI
Patterns in how people learn about personal finance
A typical user journey
Insights
People want clarity, not control. They’re not asking AI to decide, they want it to help them understand options faster and frame their thinking.
Designing a framework that allows structured exploration
These insights showed that users aren’t asking for decisions, they want structured exploration.
That informed a system where LLMs act as copilots, not advisors, leading to the framework below.
Structured prompts help users ask better questions
The quality of the answer depends upon the structure of the prompt.
Intent + context (e.g., “I’m a beginner with $500/month to invest”)
Parameters (time horizon, risk tolerance)
Trusted financial source filters
System prompt should include parsing through the user's financial data and web search trusted sources for the latest developments and market situations, with consent
Structured, explainable answers builds trust
Building credibility and showing process:
The reasoning behind the output
Source data and confidence level
Suggested follow-up prompts for deeper learning
Re-prompts stemming from guardrails
Avoids misuse, wrong information and regulatory risks by:
Flagging ambiguity and asks for clarification
Recommending alternative questions in case the answer yields a high perplexity score
Avoiding advice on securities, taxes, or personal recommendations
Human oversight
For high-stakes situations that exceed a certain threshold, human advisors should be involved in making the final decision after initial brainstorming using the LLM.
Summaries and analytics for each customer are presented to the human financial advisor for greater personalization and relationship enhancement.
Minimizing hallucination and deployment cost
Agentic AI with LLM, RAG, other tools
Limiting the use of LLMs to where they perform best:
Summarizing complex information
Parsing patterns across diverse inputs
Holding conversational memory
Multi-turn conversations for natural language UI
Building context for personalization
One of the core frictions in AI-based financial tools is that users must explain their situation repeatedly: risk profile, income, goals, etc. But that effort creates drop-off and doubt.
To solve this, I designed for permission-based context sharing: a model that allows the assistant to automatically understand key aspects of a user’s financial life, only when the user explicitly consents.
Banks: Connect trusted, regulated data with consent
Banks already hold a wealth of structured financial information: spending history, income patterns, savings behavior, and demographic data.
With user permission, this data can be leveraged to:
Auto-populate financial context (e.g., income range, recent transactions)
Surface relevant questions or planning scenarios
Offer examples grounded in the user’s actual financial footprint
However, while people trust banks to store and secure money, they’re often skeptical of advice, especially when it may be linked to selling financial products.
Fintech Platforms: Context Through Connected APIs
Fintech products already offer integrations that make personalization seamless. Using APIs like Plaid, Google Calendar, or device location, the assistant could:
Pull account balances and categorize spending trends
Connect with life events (e.g., a move, job change) for timely advice
Tailor responses based on behavior
This shifts the mental load from “Tell me your situation” to “Confirm this reflects your situation.”
Interfaces that demonstrate the framework
An LLM-powered Financial Guide in the banking ecosystem to help retail investors explore personalized investment strategies
Fig. Guided prompts and permissions for better answers
Fig. Clarifications and guardrails
Fig. Structured answers
The Outcomes
The validated product framework aligned with user behaviors and technical constraints, was adopted for continued internal development by the client team.
Next steps
Prototype a conversational design flow
Develop high-fidelity prototypes for the interface
Run usability tests