Finora
AI Assistant for Investors
INVEST SHARP TRACK EVERYTHING STRESS NOTHING
2026
How I designed a product that shortens the path from "I don't understand my portfolio" to "I see what to do", using product-thinking logic, not just visual design
SERVICES
Product Designer
We had a problem
Investment apps show data. Charts, numbers, percentages, dozens of widgets on a single screen. But data ≠ understanding.
The user can't answer a simple question "is everything okay with my money right now?" without at least 3–5 taps and doing the mental math themselves.
This isn't a visual problem. It's a product architecture problem: too much raw data, not enough ready-made conclusions.
We found the data
Competitive audit of 6 investing/trading apps (Robinhood, eToro, Public, Revolut Invest, etc.) - counting screens and taps required to answer "how is my portfolio doing." 10 contextual interviews with retail investors (target segment: 25–45, portfolio size $5k–$150k, non-professional traders) - not looking for "what do you like," but for the exact moment people get lost and close the app.
Key finding (a pattern that repeated in 8 out of 10 conversations): "I open the app, I see numbers, but I don't know if that's good or bad. I end up googling it or asking friends in a chat."
We formed hypotheses and prioritized them using a lightweight RICE-style approach.
If we replace the "dashboard of charts" with an AI assistant that turns data into a concrete takeaway, then users will open the app more often with intent and the share of users completing a financial action, because we remove the step where the user has to interpret the data themselves - which was the main drop-off point.
A secondary hypothesis that also fed into prioritization:
Clarity = trust. In a financial product, clarity is a conversion metric, not just a feeling.
We forecasted the business impact
If the hypothesis holds at a realistic level (in line with industry cases of dashboard/onboarding simplification in fintech, which typically show +15–30% activation lift): Higher Activation Rate → higher D30 Retention (a user who gets value from a takeaway once, rather than from raw data, comes back for another takeaway) Higher Retention → higher LTV per user (in a subscription/commission-based fintech model, retention is a direct revenue driver) Lower cognitive load → fewer support tickets asking "what does this number mean" → lower operating costs
Why this matters for a hiring team
I don't just "make screens look good." I follow the same path a PM would: from problem to data, from data to prioritization, from prioritization to a measurable experiment.
