UX/UI Design
Enterprise SaaS
AI Assisted Workflows
The Challenge
002
Transaction Screening is a critical part of anti-money laundering operations. Analysts investigate payment screening hits to determine whether transactions should be released, blocked or escalated.
The existing experience had evolved over many years, resulting in fragmented workflows, inconsistent interaction patterns and a heavy cognitive burden on analysts. Reviewing a single hit often required navigating multiple disconnected views while manually piecing together the information required to make a decision.
I led the redesign of the end-to-end investigation experience, creating a modern workflow that simplified decision-making, improved consistency across the platform and became a key capability demonstrated during enterprise sales engagements.
Outcomes
350% increase in adoption, helping shift the organisation toward self-service data consumption.
Reduced creation time from over 15 minutes to under 3 minutes, lowering the barrier for new producers.
I led the end-to-end redesign of the Transaction Screening experience, partnering closely with Product, Engineering, Data Science and subject matter experts to understand analyst workflows, identify usability challenges and design a scalable investigation experience from discovery through developer handover.
The Challenge
002
01
Available data is scattered across the organisation, difficult to discover what's available and who can use it
02
The process of creating a data product is long and highly technical, limiting the amount of users who can create
03
Lack of access control processes in place for existing data, relying on due diligence of data owners
The Challenge
002
I interviewed a variety of data producers and consumers to understand the current data product creation process and process for discovering and consuming data across the organisation. This helped identify various pain points and opportunities to help shape the solution
Conducted user interviews with 8 producers and consumers to understand the creation, discovery and consumption process
Conducted weekly workshops with SME's, and users within the data practice to review progress and encourage collaboration with the project
Because access to users was limited, we relied on short research cycles, weekly workshops and rapid validation with subject matter experts to continuously refine the product.
Three themes consistently emerged. Discovery relied on word of mouth, creating a data product required over 15 minutes of API configuration, and requesting access was a slow manual process. These findings became the foundation for the product strategy.
Rather than inventing new interaction patterns, I deliberately borrowed familiar retail concepts such as catalogues, baskets and checkout. These existing mental models reduced learning time while making a highly technical workflow feel approachable.
Key Product Decisions
04
Instead of distributing information across multiple tabs, I consolidated investigation into a single workspace where analysts could review hits, supporting evidence and make decisions without losing context.
02 Progressive Information Hierarchy
The average time for creating a data product was around 15 minutes or more, and required highly skilled coding ability in order to create it. We wanted to significantly reduce the time taken but also make the process more accessible, attracting more producers to the platform.
Breaking creation into logical stages reduced cognitive load while allowing users to save progress and collaborate asynchronously. This transformed a technical workflow into something approachable for non-developers.
03 Agentic Support
The complexities of data products required a highly customisable experience, what platforms to consume with?, which version of the data?, which output port type do you want to consume from?
Reflection
05
The most difficult part of this project wasn't redesigning the interface—it was reducing cognitive load without reducing analyst confidence.
Every design decision balanced speed with trust. Analysts needed enough information to make confident decisions, but not so much that investigations became overwhelming. This project reinforced the importance of designing around user intent rather than underlying system architecture and established patterns that continue to influence the wider platform.
