The challenge
Visa's fraud and risk products had been built, designed, and sold independently. Sellers struggled to explain how they connected, while clients were left to piece together the broader story themselves.
The challenge wasn't simply to combine five interfaces. We needed to understand how these products, users, data, and workflows connected — and define what a unified fraud experience should actually mean.
From fragmented products to a unified fraud lifecycle
Before · Organized by product
The gap: No single product connected risk signals across the lifecycle — yet that shared context was essential to a unified experience.
After · Organized by fraud lifecycle
The outcome
Research helped shift the team's organizing model from a collection of individual products to a unified fraud lifecycle. That reframe shaped 10 candidate experience areas, which were narrowed to five product focus areas with product alignment; select experiences have since moved into engineering.
10 → 5
candidate experience areas narrowed to focus areas
Aligned
five focus areas green-lit with product
In build
select experiences moving into engineering
Building alignment while the research was still unfolding
The sprint began with a broad mandate from product leadership: move from individual point solutions toward a unified platform experience. The path to get there, however, was still undefined.
With only 20 days, I couldn't treat research as a phase that happened before design. I structured the work to build understanding progressively while keeping the team aligned as new information emerged.
How I moved fast
Instead of waiting until the end of the study to synthesize and share findings, I built multiple feedback loops into the 20-day sprint, moving insights into design as the research was still unfolding.
Research
Design
Session-level
Research summary + shared research hub update
Daily
Research × Design synthesis + working session
2× week
Emerging insights + design implications shared with broader team
Weekly
Cross-session synthesis + shared research hub refresh
2× SPRINT
Cross-functional alignment checkpoints
Research wasn't followed by synthesis followed by design. They were happening concurrently.
Key milestones
Kickoff
Day 1
Align on goals
Foundation
Days 2–5
Product + domain
Expansion
Days 6–10
Users + regions
Pressure-test
Days 11–14
Sales + marketing
Alignment
Day 15
Decide + commit
Kick off around the mission
Broader team kickoff · 12 attendees
Aligned the broader team around a shared mission: move beyond individual products and define what a unified fraud experience could become.
Build the foundation
Stakeholder interviews · Product + domain SMEs · 6 participants
I started with PMs and domain SMEs to understand each product, its users, priorities, and constraints — while building a shared foundation across the sprint team.
Expand the perspective
Stakeholder interviews · Users + regional stakeholders · 4 participants
Building on that foundation, I spoke with users and stakeholders across ANZ, North America, and Europe to identify where needs converged — and where workflows and market context differed.
Pressure-test the story
Stakeholder interviews · Sales + marketing · 3 participants
I brought in sales, marketing, and go-to-market perspectives to understand how the portfolio was communicated and combined for clients — and what shaped client purchasing decisions.
Define design directions
Working sessions · Research → focus areas
I synthesized patterns into candidate experience areas, which the team narrowed into focus areas that shaped the prototype.
Build shared conviction
Broader team alignment · 12 attendees
I brought emerging findings and design directions back to product stakeholders, not simply for approval, but to challenge the synthesis and surface missing context. Their feedback surfaced three new sub-findings, which I incorporated back into the synthesis.
Using AI to shorten the distance from research to decision
DraftWith research and design moving in parallel, I built AI into the workflow to reduce the operational work between conversations, documentation, synthesis, and team alignment — giving me more time to focus on interpretation and product implications.
AI-assisted synthesis workflow
Raw session inputs
- Transcript
- Researcher notes
Claude
Structured session outputs
- Session summary
- Key highlights
- Interview progress
Mission Hub
Automatically updated
- Interview progress
- Session learnings
Researcher synthesis
- Connect patterns
- Reconcile tensions
- Evolve cross-product themes
AI kept the evidence moving. I focused on deciding what it meant.
AI-assisted alignment workflow
Alignment meeting input
- Transcript
- Comments in chat
Claude
- Process inputs
- Identify each person's comments
FigJam
Via Figma MCP
- Map each person's comments tagged by color
Team review
Daily sync
- Review outputs together
- Debrief takeaways and next steps
After each alignment meeting, Claude processed the transcript and chat comments; Figma MCP pushed the results as color-coded FigJam stickies, so the team could review and debrief together during the daily sync.
From research to strategic direction
Across 13 stakeholder interviews, I synthesized six cross-product themes spanning workflows, data, feedback loops, performance, AI, and commercial opportunity. Three became particularly important in shaping the unified vision.
Three insights that shaped the vision
Making the invisible visible: The opportunity wasn't simply to connect five products. It was to piece together a coherent fraud story across the lifecycle — carrying context forward, feeding outcomes back, and making the full picture visible to users and clients.
Fragmentation went deeper than the interface
Platform-level insight
What we heard
Each product was optimized around a different part of the fraud journey, with signals and information often remaining within individual product boundaries.
What I connected
A signal generated upstream could lose context as a transaction moved downstream. The problem wasn't simply five fragmented interfaces — it was fragmented context.
What it meant
A unified experience couldn't just place existing products next to each other. It needed shared context across the fraud lifecycle, allowing each stage to inform what happened next.
The system couldn't learn from its own outcomes
System-level insight
What we heard
Fraud could be confirmed through case management, but those outcomes weren't consistently connected back to the rules that originally flagged the transaction.
What I connected
The lifecycle had a broken feedback loop: teams could learn whether something was actually fraud, but that knowledge didn't systematically flow back upstream to improve future detection.
What it meant
Unification created an opportunity to close the loop between detection, investigation, and outcomes — so confirmed fraud could inform how the system performed over time.
Fragmentation was also a commercial problem
Business-level insight
What we heard
Sellers pitched individual products rather than a coherent coverage story, while clients couldn't easily see which parts of their payment journey were protected — or where gaps remained.
What I connected
The portfolio's fragmentation wasn't only creating an experience problem. It was making the value of the broader platform harder to communicate and limiting conversations about coverage gaps.
What it meant
A unified lifecycle could become a commercial asset — helping sellers show clients what they have, what's missing, and where additional capabilities could add value.
From research direction to product momentum
The sprint culminated in a unified vision and working prototype — but its impact extended beyond the artifacts. Research shaped how the platform was organized, what the team prioritized, and what moved forward into execution.
Product direction
5 focus areas green-lit
Research helped shift the platform from a product-by-product model toward a unified fraud lifecycle, narrowing 10 candidate experience areas into five product focus areas with product alignment.
Into execution
Select experiences already in build
The vision moved beyond recommendation. Design translated the five focus areas into a working prototype, with engineering already building select experiences.
Beyond the project
A faster research model adopted by the broader team
The rapid-cycle approach I refined during the sprint — continuous synthesis, shared visibility, and ongoing alignment rather than an end-only readout — was adopted by the broader research team beyond this project.
Commercial opportunity
A coherent demoable platform story
The unified prototype created a coherent, demoable platform story for sales — addressing a gap surfaced in research around how the portfolio was communicated to clients.
What I'd carry forward
This sprint changed how I think about research in fast-moving product work. Three lessons have stayed with me.
Alignment is part of the research
Sharing emerging insights early gave stakeholders space to challenge and strengthen the synthesis.
I’d continue designing alignment into the research process rather than treating it as an end-stage activity.
Speed and rigor aren't opposites
Under a 20-day constraint, rigor meant sequencing the research, making tradeoffs visible, and continuing to learn as decisions moved forward — rather than waiting for perfect information.
AI accelerates synthesis; judgment creates strategy
AI reduced the operational burden of processing research at speed. The strategic value still came from connecting motivations, identifying system-level patterns, and interpreting what they meant for the product.
AI accelerated the starting point. It didn't replace the interpretive leap from evidence to strategy.