We couldn’t design for a population we didn’t understand, so I pivoted our strategy away from pure market analytics toward deep, investigative ethnography. We (3 researchers) conducted 1:1 interviews with 50 participants, focusing specifically on marginalized groups, young adults, immigrants, and low-income users, to understand the emotional weight of financial exclusion.

This qualitative work was paired with rigorous quantitative analysis to validate the scale of the opportunity. We used frameworks like the Customer Pain Scale, which revealed that the emotional barrier around “credit invisibility” was a staggering 10/10 for many participants. However, we also encountered the “Messy Middle”: stakeholders were skeptical that users would actually consent to deep bank linking. To address this, we used Stakeholder Mapping to expose the misalignment between perceived user risk and actual user desperation for credit.

The insights that emerged forced us to shift our direction. Initially, we thought the problem was technical: how to pipe data from utility companies to Experian. But the research showed it was a Trust & Value Exchange problem. Users were terrified of linking their bank accounts. We conducted iterative usability testing with 28 users to refine our approach, discovering that transparency was a functional requirement for conversion.

• Insight: Reliability is already there, it’s just invisible. Our research showed that 90% of participants consistently made utility or rent payments, yet they remained “thin-file” consumers. They weren’t risky; they were just undocumented by the current system. Implication for Design: The design needed to function as a data validation layer, not a behavior modifier. We didn’t need to teach users to pay bills; we simply needed a mechanism to authenticate the payments they were already making.
• Insight: Transparency is the currency of trust. Meaning: In usability testing, 92% of users explicitly valued transparency over speed or visual polish. The “Conversion Cliff” was located exactly at the bank login screen. Implication for Design: We had to over-communicate. The design flows needed to be explicit about what data was being accessed, why, and how it would be used, and to adhere strictly to privacy standards to mitigate the perceived risk.
• Insight: The solution must be mobile-first. Meaning: 88% of our target demographic preferred managing their finances via mobile devices. Implication for Design: The complex task of linking bank accounts and parsing transaction histories had to be simplified into a linear, thumb-friendly mobile flow. We streamlined the IA to reduce decision fatigue, ensuring the experience felt as lightweight as a text message.

Early on, to measure the design objectives, our team at Finicity had to understand the product team’s goal at Experian and their key objectives.  From our sessions together, we realized early on that we couldn’t just track “sign-ups”; we needed to measure the health of the entire funnel from a design perspective.

• UX Quality KPIs (The Input): At the foundational level, we focused on Trust Signals and Ease of Integration. We measured this using usability metrics, aiming for a 92% trust rating for the sensitive bank-linking flow. If the “Perceived Security” score dropped here, the funnel would break before data capture could begin.
• Experience Performance KPIs (The Output): Moving up the tree, we looked at Link Success Rate and Time-to-Data-Capture. We aimed for a 10% higher completion rate in the onboarding flow. Crucially, we measured the “Match Rate”—the percentage of scans that successfully identified a utility payment. This was our proxy for “Product Value,” confirming the system was delivering on its promise.
• Business / Organizational Impact KPIs (The Outcome): Finally, these fed into the hard metrics: Origination Velocity and Partner Adoption. By validating data upfront, we aimed to reduce loan origination timelines by 8–12 days. This reduction in “Time-to-Decision” was the metric that secured buy-in from our institutional partners, effectively lowering their operational costs.

The path from concept to reality was paved with iterative prototyping loops, where we constantly balanced user desire for simplicity with the rigid constraints of fintech compliance. We started with low-fidelity flows focused entirely on the “handshake”—the moment a user consents to link their bank account.

We prioritized prototyping the secure bank linking and score visualization features first. Testing these early prototypes with 28 users revealed that while users wanted the “boost,” they were skeptical of the mechanism. This led to a significant refinement: we introduced “progressive disclosure” flows that visually demonstrated privacy and compliance (FCRA, SOC 2) before asking for credentials.

Collaboration with engineering was tight. We established a “Design-to-Dev” handshake to ensure our flows could interface with 30+ financial institutions via Finicity’s API without latency spikes. We also implemented a “Brand Mirroring” strategy in the UI. As users selected their bank, the interface would subtly adapt to that institution’s specific color palette (e.g., shifting to Wells Fargo red or Chase blue). This visual continuity reduced the cognitive dissonance of the “handshake,” making the third-party connection feel like a native extension of their own banking app.

The implementation strategy was phased. We rolled out with utility payments first, deliberately narrowing the scope to ensure we could deliver on the promise of an instant score boost. This “crawl, walk, run” approach allowed us to iron out the complexities of data integration without overwhelming the user. Ultimately, the prototypes evolved into a live product that not only solved users’ needs but also integrated seamlessly into partner ecosystems, directly impacting the “Time-to-Decision” KPI.