Norwegian company / Fintech / 2026
Finlens
Stock research
made easier to understand.
I designed Finlens for a Norwegian company, with Norwegian retail investors (target audience) as its first audience and other countries to follow. It brings the recommendation, risks and supporting research into one place.
More information.
Still no clear investment decision.
Beginner investors (target users) face charts, financial ratios and market commentary before they know how to interpret them. More research can mean more questions, rather than a clear basis for an informed investment decision.
For a beginner, access to research is only the starting point. The harder questions are: what does this mean for this stock, which evidence matters, and what are the risks? Without that connection, the detail becomes overwhelming and the decision remains unclear.
Solution
A clear verdict.
The evidence to understand it.
Finlens leads with a buy, hold or avoid verdict, backed by the research, statistics and reasoning behind it. Confidence, risks and investment horizon stay visible, while deeper evidence is available for investors (target users) who want to investigate further.
This case study presents the design and prototype. Screen counts describe the work delivered, rather than measured business performance. Finlens is currently in internal testing with a selected user group, so measured business performance is not yet available.
- Confidence level
- Key risks
- Supporting research
Explain the recommendation
A clear verdict with the uncertainty visible beside it.
Show the supporting research
Trace every call back to financials, valuation, technicals and sentiment.
Make charts easier to explore
Charts that respond to questions through tooltips and timeframes.
Show value before asking users to pay
Let investors (target users) experience useful research before asking them to upgrade.
Business impact
Internal pilot / Selected users
Make research clarity a reason to return.
Finlens brings a recommendation, its uncertainty and supporting research into one experience. The business goal is to help investors (target users) experience useful research, build trust and see a reason to keep using the product before considering an upgrade.
- Current evidence
- The product is in internal usability testing with selected users. The design, interactive prototype and reusable system are delivered; retention and paid conversion have not been measured here.
- What to measure next
- Validate first-research completion, understanding of the recommendation, repeat research sessions and the path from demonstrated value to upgrade.
Target audience
Beginner first.
Room to go deeper.
Finlens is designed for Norwegian retail investors (target audience), with a primary focus on beginners and a new generation of investors (target users). The same experience supports more experienced users as their research needs grow.
New to investing.
Ready to understand it.
People who want to make informed investment decisions but need help interpreting financial information. The product should help them understand a recommendation, weigh the risks and know what to check next.
- Plain-language recommendations with risk and confidence in view
- A clear next step into the supporting research
- A way to save a stock before deciding whether to invest
More evidence.
More control.
Regular researchers and long-term holders need depth without starting over in another tool. Named evidence tabs, interactive charts, comparison and portfolio views let them investigate the recommendation for themselves.
- Financials and valuation for checking the AI’s reasoning
- Comparison and charts for evaluating different stocks
- Holdings and risk views for deciding what to review next
Research & evaluation
Understanding investors (target users).
The target audience is Norwegian retail investors (target audience). This section brings together a source-backed competitor review, a reconstructed interview guide and usability scenarios tied to the current design.
Competitor analysis
Source-backed review of relevant products.
Investor interviews (target users)
A reconstructed guide for Norwegian retail investors (target audience).
Usability evaluation
Task scenarios connected to the current design.
Behavior analysis
PostHog review plan with illustrative overlays.
Competitor analysis
Products relevant to Norwegian investors (target users).
Compare how people discover a stock, interpret research and monitor a portfolio. The final column is a design question for Finlens, not a claim that a competitor lacks that capability.
| Product | Publicly described offering | Design question for Finlens |
|---|---|---|
| Nordnet (external source)Broker serving Norway | Norwegian and international stock lists, filters, company data and key figures. | How can Finlens help someone interpret the numbers after finding a stock? |
| DNB (external source)Norwegian bank and broker | Share trading alongside analyst recommendations, company news and research. | How can a recommendation and its reasoning stay connected throughout the experience? |
| Pareto Securities (external source)Norwegian market benchmark | A trading and analysis platform with Nordic and US equities, market information and analyst research. | How can detailed research remain approachable for a less experienced investor (target user)? |
| Simply Wall St (external source)International research benchmark | Visual stock research, discovery tools, portfolio analysis and market alerts. | How can Finlens connect a visual overview to a specific, explainable recommendation? |
Public sources reviewed on 17 October 2026. These are relevant benchmarks, not a market-share ranking. Simply Wall St is included as an international reference.
Interview guide / reconstructed
Questions that shape Finlens.
Use the same core questions with every Norwegian investor (target user), then follow up on the tasks that matter to their persona. This keeps the answers comparable while testing different needs. The guide below is reconstructed, not an interview transcript.
Core questions for every investor (target user)
Understanding the AI verdictOpen the Finlens stock summary. What do you think the recommendation is asking you to consider, and which information would you check before deciding?
Follow-up: What, if anything, is missing before you would investigate this stock further?
Design decision informed: Decides whether the verdict, risk and time horizon belong together in the first screen.
Confidence versus expected returnThe summary shows 92% confidence and an estimated return. What does each number mean to you, and what do you think neither number can promise?
Follow-up: Which wording or explanation would help you tell the two apart?
Design decision informed: Decides how confidence and return estimates are labeled, explained and separated.
Norwegian and international investingWhen reading this Finlens stock report, what would you need to check about the market, currency and language before using the information?
Follow-up: Point to where you would expect to find that information in this screen.
Design decision informed: Sets requirements for Norwegian defaults, currency labels and international-market context.
Follow-ups by investor persona (target users)
Explain an unfamiliar termOn the Summary screen, point to any term or chart you cannot explain. What would you expect to happen if you wanted help?
Follow-up: Which explanation would help you continue without leaving Finlens?
Design decision informed: Determines where plain-language explanations and contextual chart help are needed.
Save without buyingYou want to revisit this stock next week but do not want to own it yet. Where would you go, and what would you expect to happen?
Follow-up: After saving it, would you expect any money or holdings to change?
Design decision informed: Tests the distinction between Add to Watchlist and recording a holding in Portfolio.
Choose what to read nextWhat would you open next to understand why Finlens gave this recommendation? Tell me why you chose it.
Follow-up: What answer would you expect to find in that tab?
Design decision informed: Checks whether the evidence-tab names provide a clear next step for a beginner.
Usability testing / reconstructed scenarios
Connect each concern to a design response.
These scenarios explain what to test and how the current design addresses the risk. They do not establish that a previous version failed or that the change improved a measured outcome.
Explain a recommendation
Someone may read only the large verdict and miss the uncertainty.
Confidence, risk and investment horizon sit beside the recommendation. The research is split into named tabs.
Ask the participant to explain the recommendation and one reason it could be wrong.
Review tab clicks, scroll depth and the recording around the explanation task.
PostHog / analysis approach
Look at the behavior behind the click.
PostHog was identified as the project’s analytics tool. Its original project data is unavailable, so the examples below show the intended review method. They are not screenshots from a PostHog workspace.
Example areas to inspect: the recommendation, evidence tabs and save action. Hotspots demonstrate the analysis method; they are not counts or observations.
Example of how scroll attention could be visualized. A real PostHog scrollmap is needed before claiming where people stop reading.
Observe the complete task
Use recordings alongside click and scroll maps. A hotspot shows interaction; it does not explain why someone clicked.
Track the relevant steps
Suggested events: research opened, evidence tab selected, watchlist saved and transaction modal opened. These are an instrumentation plan, not verified captured events.
Validate before claiming improvement
Compare the same task and audience before and after a change. Keep recordings, task outcomes and participant context with the conclusion.
Prototype scope and next steps.
- The header search and AI Assistant are placeholders. Research and AI Insights in the sidebar are the working routes.
- Market rows currently open representative stock research. Market reports and loading screens are designs only.
- The prototype uses international sample-market data. Norwegian currency, market defaults and language are part of the next localization review.
These limitations describe the current prototype. User testing will guide the next iteration.
Norwegian investor personas (target users)
Representative profiles for the first market. Persona names and statements are illustrative, not interview transcripts.
Ingrid Larsen
27 / Product manager
“I have the money and the apps. What I don’t have is the confidence to press buy.”
NeedsA clear explanation, a reason to trust it, and a manageable weekly habit.
Design responsePlain-language verdicts with visible confidence and risk.
Erik Johansen
34 / Software engineer
“I’ll read a 10-K if I must. I just don’t have ten hours every weekend.”
NeedsLess time spent researching, with enough detail to check the recommendation.
Design responseSeven evidence tabs, interactive charts and side-by-side comparison.
Lars Berg
46 / Business owner
“I bought good companies in 2021. I honestly couldn’t tell you what they’re worth now.”
NeedsA second opinion on stale holdings, concentration and changing risks.
Design responsePortfolio monitoring, risk breakdowns and a contextual AI copilot.
Design strategy
The design decisions behind Finlens.
I focused on helping investors (target users) understand the recommendation, check the research and see where the AI could be wrong.
The frictionA dashboard full of ratios leaves investors (target users) doing the hardest work themselves.
My design responseLead with buy, hold or avoid. Keep the confidence level, risks and investment horizon beside the recommendation.
Visual design decision
Why the product uses bright green.
The energetic green palette gives Finlens a contemporary, approachable feel for a younger generation of investors (target users). Clear layouts, readable type and familiar controls keep the focus on understanding the information.
Working within the project constraints.
AI output needed to stay identifiable as research, with confidence and risk visible. Metered market data made deep reports expensive, so I reserved them for moments of intent. As the solo designer, I built reusable chart components early to keep a 35-screen experience consistent.
Information architecture & flow
From first visit to portfolio tracking.
Build a profile. Discover a stock. Inspect the evidence. Keep monitoring. The journey connects the first question to the next decision.
Set up an investor profile
Five steps, goal, risk, amount, markets, ending in an AI summary of who you are as an investor. You correct it before it scores anything.
Information architecture
Product sitemap
How the main product areas connect and the screens within each area. Select a screen node to read its purpose.
Onboarding: Welcome, Goal, Risk, Amount, Markets, AI profile summary.
Discover
Top markets
Sample market rows open representative stock research in this prototype.
Stock lists
Open a stock or save it to a watchlist.
Research
Markets & sectors
Browse sample market and sector tables.
Search stocks
Search a stock and open its research workspace.
Stock research
Opened from stock search, Discover, holdings or a watchlist. Breadcrumbs return to Research or Stocks.
Summary
Recommendation, confidence, risk and an interactive price chart.
Financials
Revenue, financial statements and margins.
Valuation
Fair value and valuation multiples.
Technical Analysis
Price indicators and technical signals.
Sentiment
Analyst and sentiment summaries.
News
Stock-related headlines.
AI Notes
The sample AI research summary.
Compare
Stock comparison
Compare the sample stock columns and toggle which columns are visible.
Portfolio
Overview & holdings
Portfolio performance chart, holdings and factor radar. Holdings open stock research.
Add transaction
Record a sample holding. This prototype does not place brokerage orders.
Record buy / sell
Update local sample holdings through the trade modal.
Watchlist
Saved lists
Switch lists, remove a stock or create a watchlist.
Open stock research
Open any saved stock in the shared research workspace.
AI Insights
AI Copilot
The first suggested question opens a scripted sample answer. Free-text chat and other suggestions are not implemented.
Settings
Preferences & alerts
Sample preferences and local notification toggles.
Security
Sample email and two-factor controls. Account changes are not implemented.
Plan & billing
Manage plan opens Choose your plan, with a return path to Settings.
Designed screens outside the interactive flow
Market report, report loading and the standalone account profile are available in the screen gallery. They are not implemented routes in this prototype. Import, live alerts and account editing are also outside its scope.
Cross-links in the prototype
Discover, search results, holdings and saved stocks open the same stock research workspace. Manage plan opens Plans from Settings. The header’s search and AI Assistant controls are placeholders; use Research and AI Insights in the sidebar.
The design system behind Finlens
A design system.
Ready to build.
A chart-heavy product needs more than a collection of screens. I built the foundations, component states and code handoff together, so the experience stays consistent as the product grows.
- Color, typography and spacing foundations
- Charts treated as reusable components
- Documented states and developer-ready handoff
High
<VerdictCard confidence={data} />My working process
How I designed the product.
I used ChatGPT to write structured briefs and Claude Code connected to Figma through MCP to build the designs. I reviewed the work, tested the flows and made the design decisions at each stage.
Define the problem and project scope
I started by understanding what the business needed and where investors (target users) were getting stuck.
- Document the business goals, user problems and limits on market-data usage in Notion.
- Map the main tasks: find a stock, understand the recommendation and track a portfolio.
- Use the interview, diary and product-review findings to decide which problems the first version should address.
Output: A clear brief with the main user tasks, product goals and constraints.
Research the users, competitors and key tasks
Research shapes the design brief before wireframing. I connect the Norwegian investor context, competitor patterns and the questions the product needs to answer.
- Review products relevant to Norwegian investors (target users), including Nordnet, DNB, Pareto Securities and Simply Wall St. Compare discovery, stock research and portfolio tasks.
- Use a shared Finlens interview guide, with follow-ups for first-time investors (target users), regular researchers and long-term holders. Link each question to a design decision.
- Turn the research questions into tasks for prototype evaluation, then identify what to review in PostHog recordings, click maps and scroll maps.
Output: Research questions and design priorities for the recommendation, evidence tabs, watchlist and portfolio. Reconstructed examples and unavailable original evidence are identified in the research section.
Write a structured design brief
I used ChatGPT to organize the product brief into instructions I could review before passing them to Claude Code.
- Describe the target users, research findings and intended product behavior.
- Ask ChatGPT to structure the scope, screen list, user flows and constraints in a Markdown brief.
- Review and edit the brief so the requirements reflect my design decisions.
Output: A reviewed Markdown brief for the wireframing work.
Create wireframes in Figma through MCP
I connected Claude Code to Figma through MCP. The wireframes were created in Figma, using the structured brief as a starting point.
- Give Claude Code the reviewed brief and connect it to the Figma file through MCP.
- Create low-fidelity frames for onboarding, stock research and portfolio tracking in Figma.
- Review the layouts and screen sequence, then revise the prompt and frames where the flow needs work.
Output: Figma wireframes ready for flow review and user feedback.
Build a prototype, test and revise
I built a fast interactive prototype with Claude Code for this project, then reviewed the flows with users and business owners. I sometimes use Codex for prototyping too; for Finlens I used Claude Code.
- Use the Figma wireframes as the reference and build the interactive prototype in Claude Code, including navigation, inputs and the main screen states.
- Walk through the core tasks in the prototype with users and business owners.
- Record confusing labels, missing steps and unclear information in Notion.
- Revise the prototype and Figma wireframes together, then review the updated flow before continuing.
Output: An interactive prototype and revised Figma wireframes with the main flow issues addressed.
Set up the visual foundations
Once the flow was stable, I built the visual foundations in Figma using the product’s green brand color.
- Define the color roles, typography and spacing needed across the screens.
- Use Claude Code through Figma MCP to create the foundations in the Figma file.
- Review contrast, hierarchy and chart styling, then adjust the tokens before building components.
Output: Reusable color, typography, spacing and chart foundations.
Build and review the component library
I used the validated wireframes to identify the components the product actually needed.
- List the shared elements, including navigation, cards, inputs, recommendations and charts.
- Structure the component requirements with ChatGPT, then use Claude Code through Figma MCP to build them in Figma.
- Review variants, states, spacing and token use so the same component works across different screens.
Output: A Figma component library with reusable styles, variants and states.
Assemble and refine the final screens
I used the component library to build the 35 high-fidelity screens, then reviewed how the full experience worked together.
- Use Claude Code through Figma MCP to assemble screens from the approved components.
- Review the visual hierarchy, chart readability, copy and consistency across each flow.
- Test the journey again, refine the screens and review them with the business owners.
Output: 35 high-fidelity screens and a reviewed end-to-end experience.
Document components and prepare the handoff
I prepared a Storybook reference so developers could inspect the components and understand how to use them.
- Bring the component library into Storybook as a shared implementation reference.
- Document component states, usage guidance and code alongside the visual examples.
- Review the library against the Figma designs and hand over the screens, components and supporting documentation.
Output: A documented component library and developer handoff.
Accessibility / Design & developer handoff
Financial clarity for everyone.
WCAG 2.2 Level AA guides the design and implementation requirements. Recommendations, risk and price changes pair color with labels, signs and values. Charts need a readable summary and an equivalent data view; hover alone must never hide essential research.
Readable, beyond color
Text contrast: at least 4.5:1, or 3:1 for large text. Essential control boundaries and state indicators: 3:1. Labels and symbols carry meaning alongside color.
1.4.1 · 1.4.3 · 1.4.11Keyboard & focus
Every action works by keyboard, with visible focus that stays in view. Dialogs contain focus, support Escape and return focus to the trigger. Web targets meet the 24 × 24 CSS px minimum or its spacing exception.
2.1.1 · 2.1.2 · 2.4.7 · 2.4.11 · 2.5.8Clear names & feedback
Controls expose their name, role and state. Inputs have persistent labels and linked error messages. Async results use status announcements without moving focus.
1.3.1 · 3.3.1 · 3.3.2 · 4.1.2 · 4.1.3Adaptable layouts
Support text resizing to 200% and reflow at 320 CSS px without losing controls or content. Respect reduced-motion preferences and provide text alternatives for meaningful graphics.
1.1.1 · 1.4.4 · 1.4.10 · Motion preferenceRelease checklist: test every state with keyboard and screen readers, zoom and narrow layouts; measure actual foreground/background pairs. Static product screens document visual intent and do not establish app conformance. View the WCAG 2.2 requirements ↗
The final experience
Explore the product screens.
Browse the categories on the left to see what I designed. Choose a screen group, then open any screen for a closer look.
Onboarding
6 screens / Click to enlarge
Delivery & reflection
What I delivered and learned.
The work includes high-fidelity screens, reusable components, interactive charts and a working prototype.
What worked
Leading with the verdict gave the product a clear purpose. Keeping uncertainty and supporting evidence close stopped that clarity from becoming a black-box recommendation.
What I’d improve
I would test the losing states earlier: a verdict that proves wrong, a portfolio drawdown and AI notes that have aged. Those are the moments that put trust under the most pressure.
What I learned
AI can make a product more decisive. The designer’s job is to make that commitment visible, inspectable and honest about what it does not know.
