Sophisticated technology, a legacy product, a team of two
Feedzai builds AI-powered fraud and risk detection for banks and payment companies. The technology was sophisticated. The product analysts used to act on it, a legacy Alert Manager, wasn't. It didn't scale, and it couldn't handle where the business was heading.
I joined as the second designer, tasked with building the design and research practice from scratch. Analysts were making hundreds of judgment calls a day, each with real financial and reputational consequences.
Design the product. Establish the practice.
- Design Case Manager from scratch: risk and case management with rich data visualisation and self-service configuration
- Grow the design team from two people to eight
- Build a UX research practice: workshops, personas, recurrent usability testing
- Make ML output legible and actionable without a data science background
Structure before screens
Case Manager needed a structure analysts could hold in their head: nine sections, from Search and Overview to Rules and Admin, mapped before a single screen was drawn. Paper sketches and wireframes tested that structure against real flows, account recovery, rule configuration, audit trails, before anything reached engineering.
From legacy alert queue to decision workspace
With the structure validated, high-fidelity screens replaced the legacy Alert Manager one workflow at a time, each tested through moderated sessions, SUS scores and attrakdiff, so we had signal throughout the build, not just at the end.
The interface followed the analyst's decision process, not the data model. Insights translated ML output into a form compliance could read and trust: specific enough to be meaningful, honest enough not to overstate certainty.
Case Manager: evaluative research
Problem. After the formative work, we needed to evaluate usability, not assume it.
Solution. Recurring usability tests with a fixed internal cohort: moderated interviews, task success metrics, SUS and attrakdiff scores.
Usability testing sessions (July 2016 + January 2017)
Three calls that shaped the outcome
Design the AI as a collaborator, not an authority
The model score was visible, never the headline. Analysts saw the evidence first, the model's read second. They caught model errors a more automated interface would have missed.
Audit trail as a design constraint, not an afterthought
Every decision here had regulatory weight. Building the audit trail in from day one, not bolting it on as a report, shaped how every interaction was structured.
Research as a product input, not a UX deliverable
The 40+ interviews weren't a slide in a deck. They fed straight into prioritisation, IA, and interaction patterns, so the product matched how analysts actually worked, not how we assumed they did.
Components, documentation and academy
Scaling the team meant documenting the system too. A UI kit, and a Feedzai Academy training platform built with the technical writing team, kept the design team and our customers working from the same reference.
Products that analysts trusted
In a domain where bad decisions carry real financial consequences, trust was the only measure that mattered. Case Manager and Insights became defining products in Feedzai's commercial expansion.
What this changed
Feedzai is where I first had to design alongside machine learning, not just in front of it. Making opaque model output legible without eroding human judgment is a problem I've carried into every AI-adjacent project since, and it's more relevant now than it was then.
The model score was never the headline. Evidence first, then the model's assessment.