Leading three fintech products
Context
After a stretch as Product Owner, I had several offers inside the company — including a management role — and chose to return to hands-on design work. Out of the available teams, I picked the one whose mission was the shortest path from app open to a user's first placed trade — a core team with a core metric.
Three directions were already on the table — a brand-new Copy Trading product to launch from zero, a flagship AI feature that people couldn't find and didn't trust, and a user-requested alerts feature that had never been validated. Three different types of problem, one team.
What we did
Copy Trading — launch from zero
The signal came out of a week-long service-design intensive with the full team. We talked to experienced and partner traders as well as beginners, and a two-sided demand became obvious: beginners wanted to copy experienced traders' strategies and were willing to pay for it; experienced traders wanted to monetise their track record and earn additional income. The market exists, the demand was real — we decided to build.
Process (~six months)
A full competitive teardown of the major copy-trading platforms. Many iterative working sessions on roles, sections, user stories, Customer Journey Maps, user flows, and state diagrams. A second service-design session surfaced four useful findings.
Findings
Filters. Users don't need complex strategy filters.
User pattern. The journey is predictable — a beginner comes in, then either moves to independent trading or becomes a strategy master themselves.
Terminology. Standard copy-trading terminology is opaque even to experienced traders.
Control. Users want granular copy settings — to lose less — and the ability to close trades manually.
Decisions
Architecture. We debated for a long time whether to embed Copy Trading inside the existing FBS trading app or ship it as a separate one. We chose a separate mobile app. The existing app was already overloaded; stacking a new logic layer on top would make both products worse.
Design system. The existing web dashboard had no real component base — about 100 colours with no clear assignment. We assembled a minimal working kit from what was there rather than rebuilding a full system. The rewrite wasn't the battle to fight at this stage.
What shipped
A full end-to-end flow for two roles, delivered as a standalone mobile app and a web dashboard, carried through to release over many rounds of iteration with engineering.
Master (the experienced trader). Registration and verification → strategy creation → offer and monetisation forms → ongoing management of strategy, offer, and trading.
Investor (the subscriber). Strategy discovery → subscription → copy-parameter setup → copy start → analytics and fine-tuning.
AI Assistant — fix what's broken
AI Assistant already existed in the FBS app. The flow was simple: tap a button on the chart, the algorithm snapshots the timeline and returns a prediction — buy or sell, at what size. Usage was low, and conversion from an AI session into an order hovered around 16%.
We ran moderated qualitative testing and deep interviews in Indonesia and Nigeria, with both existing FBS traders and newcomers. Four systemic problems surfaced.
Problem
Discoverability. Most people couldn't find the feature. The abstract icon didn't read as AI, so they looked in the wrong sections of the app.
Indicators. The algorithm returns a more accurate prediction when MACD, Bollinger Bands, and similar indicators are active — but users didn't know they had to set them up before launching, and once the report was generated, indicators couldn't be changed.
Order creation. It wasn't clear that order parameters were prefilled automatically, and there was no way to open a position at the current market price straight from the report.
Trust. The algorithm didn't explain why it made a given call. No explanation, no trust.
Solution
Before the report. Replaced the icon with an explicit "AI" label so the entry point is unambiguous. Moved indicator and timeframe configuration up-front into the create sheet, before the report is generated. Added a FAQ that explains how the algorithm works — the direct fix for the trust problem.
After the report. Rewrote the order cards with hints about autofill and added a one-tap "Trade at market price" action. Reordered the report so the verdict is visible without scrolling, and added a risk/reward metric that tells the user how risky the prediction is. Added report history with a dedicated entry point and a results view.
Result
7-day retention around 24%. The share of AI sessions converting into an order rose from ~16% to ~24% over nine months (Nov 2024 – Aug 2025) — roughly every fourth user acting on an AI recommendation, up from every sixth.
Custom Alerts — ship from a user signal
The signal came out of the same service-design session. FBS traders can't stay in front of the screen all day but want to react to specific market events — price reaching a set level, a candle closing above or opening below that level. We looked at how competitors handle it, mapped the feature into the existing FBS mobile app, built the state diagrams, and — critically — prototyped it and validated with a UX test before engineering started.
The test showed that once users found the entry point, creating and deleting alerts was easy — discoverability was the real problem. We replaced the bell icon with an alarm clock — closer to how users think about alerts. Added alert creation from an open position, making Positions a second entry point. Linked per-instrument alerts to the full alerts list.
Result
Custom Alerts shipped in the FBS mobile app, creatable straight from the chart screen. 7-day retention 23.1% in the three months following launch (May – Jul 2025).
Result
Three directions, different in nature, same approach: launch from zero, fix what users couldn't use, and ship from a validated user signal. In each case, research came before the design, and the design was validated before the build.
All three shipped to production within the year — Copy Trading as a standalone mobile app with a web dashboard, the AI Assistant redesign in the FBS trading app, and Custom Alerts as a native feature in the FBS mobile app.