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Neo Advance sat between overdraft, cash advance, and credit building — without an established product model for any of them to follow.
I led the experience from early ambiguity through launch, defining how the product should work across banking, lending, lifecycle, and collections while guiding a two-designer team through the highest-risk decisions.
The result was a system customers could understand, teams could build against, and the business could evolve over time.
Use Advance when a chequing balance falls short.
Pay Advance when balance is due, with payment options.
Build credit when use, repay, and grow with Advance.
A clearer product model translated into stronger adoption, higher conversion, and a scalable revenue path.
32%
Activation
32% of 1,208 beta customers opted into Neo Advance within the first 2 months
2.8x
New booked users
Neo Advance increased booked users from 20 to 56 per 100 applicants based on beta results.
~$3.5M
Projected 2-year revenue
Across membership, usage fees, and downstream credit conversion at scale.
WHERE WE WERE STUCK
Neo Advance was intended to set the pattern for future lending products, but those products were still undefined. During early concept exploration, the team was split between optimizing for launch and designing for the future, blocking decisions on entry points, account IA, and shared patterns.
We tested the assumptions shaping the product and used what we learned to define clearer system rules.
We tested the assumptions shaping the product and used what we learned to define clearer system rules.
Same journey spine, different behaviours by loan structure.
A shared account framework, adapted to each loan structure.
A shared framework for different ways to pay.
WHERE WE WERE STUCK
In early hi-fi exploration, the team was stuck on how to communicate credit progression within the Advance hub—the in-app screen showing a user’s total and available credit limit. Engineering, Marketing, and Product were pushing conflicting directions, stalling key design decisions.
WHERE WE WERE STUCK
Two weeks before launch, the team was still misaligned on several key decisions. Product and Design wanted a final usability test, but leadership pushed back because testing all 10 flows and 200+ screens would take too long.
Use AI to quickly scan comparable products, reviews, and patterns when external evidence can resolve the question quickly.
I set the core interaction model and led a two-designer team through the highest-risk flows, personally owning the key paths from access to repayment.
I set the core interaction model and led a two-designer team through the highest-risk flows, personally owning the key paths from access to repayment.
I partnered with Product and Engineering to define the end-to-end payment flow across initiation, review, completion, and edge cases, while keeping the experience consistent with the broader Advance model.
I defined the lifecycle framework for eligibility, limit growth, and ongoing use, then reviewed execution across teams to keep 200+ screens coherent.
With one designer leaving and another on vacation, I independently delivered the core experience, delinquency, payments, and credit progression, while another designer supported additional lifecycle moments.
With one designer leaving and another on vacation, I independently delivered the core experience, delinquency, payments, and credit progression, while another designer supported additional lifecycle moments.
I turned a dense set of payment states, account conditions, and edge cases into a rigorous framework that made the system easier to reason about, align on, and implement consistently.
Designers navigate ambiguity not by eliminating uncertainty, but resolving the few uncertainties every downstream decision inherits.
In Neo Advance, the real risk wasn’t unanswered questions—it was letting unresolved decisions about account IA, entry points, and lending patterns propagate across 200+ screens and future products. I focused the team on resolving those structural questions first, using cross-product benchmarking and targeted research to turn them into shared rules before the design scaled.
