Case study

Reimagining Digital Investing with AI-Powered Guidance

This project explored a new model for self-directed investing by integrating AI-powered guidance into critical decision moments across the investing journey.

As a UX Designer within a team of three, I contributed to the end-to-end experience design, focusing on balancing user autonomy with contextual, intelligent support.

My Rol

User experience aligned with objectives Interaction Design Experience Architecture

The Challenge

Self-directed investing platforms give users control—but also expose them to risk, uncertainty, and cognitive overload.

Users often:

  • Struggle to understand the impact of their decisions
  • Act emotionally under market pressure
  • Lack confidence, especially in volatile scenarios

The core tension

How might we help users make better financial decisions without taking control away from them?

Framing the Opportunity

Instead of replacing decision-making, we focused on:

  • Supporting users with contextual insights
  • Increasing awareness of consequences
  • Preserving autonomy at every step

Early exploration revealed a key insight:

Users don't want automation.

They want confidence.

This led us to define a guiding principle:

Guide, don't dictate.

Approach

This engagement balanced product definition with rapid experience design.

Key activities included:

  • Collaborative workshops to align on vision and scope
  • Journey mapping across onboarding, portfolio, and trading flows
  • Rapid white-boarding and concept iteration
  • AI prototyping to explore conversational and contextual interactions
  • Development of experience principles to guide decisions

We worked iteratively, using lightweight prototypes and storytelling artifacts to align stakeholders and refine direction.

Users

We identified two behavioral extremes:

👦🏻

Baby boomers generation

  • Lower confidence in digital tools
  • Higher risk aversion
  • Prefer clarity and reassurance
  • Users who have 1MM to 500MM in household assets.
👴🏼

Digital natives generation

  • Comfortable with autonomy
  • Faster decision-making
  • More prone to overconfidence and impulsive trades

Shared need
Understanding the consequences of their actions before committing.

Key Problems Identified

  • Discoverability → Users don't know what tools are available
  • Understanding risk → Difficulty interpreting financial impact
  • Emotional decision-making → Fear and greed drive behavior
  • Trust in AI → Skepticism toward automated recommendations

The Solution

We designed a hybrid AI guidance system embedded across the investing journey.

Instead of a single feature, the solution works as a layer of intelligence that adapts to user context.

The Core Shift

From

Transaction-first investing

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To

Guided, confidence-building decision-making

Core Features

Sandbox Portfolio

A safe environment where users can simulate strategies without real risk.

Why it matters:

  • Reduces fear of making mistakes
  • Encourages exploration and learning
Sandbox Portfolio mockup

What-If Scenario Simulator

A tool that lets users explore outcomes before committing.

Why it matters:

  • Users shift from reactive to proactive thinking

Why it matters:

Why it matters:

AI Financial Guide mockup

Impact Awareness Before Trades

Before confirming a trade, users see:

  • Risk exposure changes
  • Portfolio balance impact
  • Potential downside scenarios

Why it matters:

It introduces a pause moment that reduces impulsive decisions.

What-If Simulator mockup

Goal-Based Investing Dashboard

Tracks progress toward personal milestones (retirement, education, etc.).

Why it matters:

  • Connects investing to real life
  • Creates long-term engagement
Goal-Based Dashboard mockup

Embedded AI Assistant

Goal: Make guidance always available, but never intrusive.

A conversational layer that:

  • Explains decisions in plain language
  • Answers contextual questions
  • Builds trust through transparency
AI-powered question prompts

Embedded AI Assistant

Users can choose how much guidance they want:

  • Passive insights
  • Contextual nudges
  • Deeper AI explanations/li>

This avoids the “one-size-fits-all” problem.

Goal-Based Dashboard mockup

Outcomes

While the work was exploratory and forward-looking, it delivered meaningful impact:

  • Established a clear, unified vision for AI-powered digital advice
  • Aligned stakeholders around a cohesive experience strategy
  • Defined a realistic MVP and phased roadmap
  • Produced high-fidelity prototypes to communicate complex ideas
  • Created reusable patterns for AI-assisted decision-making
Portfolio preview web application

Key Takeaways

Key Takeaways

Guidance is most effective when it respects user autonomy

Users don’t need more tools — they need help using them

Guidance is most effective when it respects user autonomy

Users don’t need more tools — they need help using them

Guidance is most effective when it respects user autonomy

Users don’t need more tools — they need help using them

What I’d Improve Next

  • Personalize guidance based on behavior over time.
  • Validate with real users in live trading environments.
  • Further reduce cognitive load in high-stress moments.

Closing Thought

Designing for financial decision-making isn’t just about usability—it’s about responsibility.

The goal isn’t to make decisions for users, but to help them make better ones.