An AI-assisted investing experience for Morningstar that connects investor priorities, allocation changes, and financial explanations within one comparison workflow.
Highlights
role
Design Strategy, UI Design, Interaction Design, Technical Collaboration, Data Visualization
team
1 UX Designer, 3 Data Scientists, 1 Software Developer, 1 Scrum Master
duration
4 weeks
The challenge
Investment tools provide charts, risk scores, and projections, but investors still need to understand how those results relate to their goals and what they might give up by choosing a different approach. Our goal was to help people build that understanding while exploring their own portfolio.
AI financial advice raised a question of trust
In a 2023 CFP Board survey, 31% of investors felt comfortable acting on AI-generated financial advice without verification. That figure reached 52% when a financial planner verified the advice.
For InvestiGenie, this raised a design question: How could investors examine the reasoning behind a portfolio proposal before deciding what to do?
Product Strategy
I separated the experience into three parts:
Element
Purpose
Financial profile
Establish the goal, timeline, and risk tolerance
Investment preference
Define the change being explored
Proposed scenario
Show an alternative and its tradeoffs
Structured controls made consequential inputs explicit and editable. The assistant remained beside the analysis, where investors could ask follow-up questions without losing context.
Technical collaborators developed the financial modeling. I designed how investors accessed, compared, and interpreted its outputs.
DECISION 1
Confirm the interpretation before generating a proposal
A sustainability preference could mean applying broad criteria, reducing specific exposures, or pursuing an investment theme. I designed distinct choices for these approaches and a scenario summary showing how the selected inputs would guide the comparison.
The tradeoff: Clarification adds an interaction, but gives investors a chance to correct an interpretation before it shapes the proposal.
DECISION 2
Make competing priorities an explicit choice
The walkthrough combines capital preservation, a five-year horizon, aggressive risk tolerance, and clean-energy investing.
I introduced a choice between prioritizing preservation and pursuing growth. The assistant explains why willingness to accept losses does not automatically determine which approach supports the investor’s goal. The investor confirms the priority before reviewing the proposal. This makes a consequential decision visible rather than hiding it within the result.
DECISION 3
Connect each change with its reason
The comparison shows current and proposed allocations, holding-level differences, and a rationale for each adjustment. Related views cover risk, five-year outcomes, and tradeoffs. In the illustrative preservation-first scenario, lower modeled downside appears alongside lower growth potential.
I kept these views within one workspace so investors could examine the same proposal from different perspectives.
The AI experience
Answer the question beside the evidence
InvestiGenie combined retrieval-augmented generation (RAG) for financial explanations with Monte Carlo portfolio modeling for exploring possible outcomes. An investor could ask, “Why prioritize preservation if my risk tolerance is aggressive?” while examining the proposed allocation.
The example answer connects the proposal to the five-year goal, explains the limited thematic exposure, and acknowledges the growth tradeoff. The assistant supports interpretation while the investor retains control.
Outcome
The team presented the original proof of concept at Morningstar. Subsequent refinement made scenario inputs, competing priorities, allocation rationale, and model limitations more explicit.
For investors
A workflow for questioning proposals and understanding their consequences.
For Morningstar
A demonstration of how its investment data and analytics could support guided exploration and more informed investor conversations.
Proposed Evaluation
I would run task-based sessions with intended users to test whether investors can explain how their priorities shaped the proposal, identify its main tradeoff, and understand that simulations offer no guarantees and saving leaves holdings unchanged.