As the sole designer, I created a 0→1 tool that helps BNY track over $9.5B in transactions. I explored data visualization and redesigned the tables, cutting transaction lookup time by 87%.
Before the redesign, users had to switch between Excel, UPP, and APY to find transaction information, taking an average of 9.3 seconds. After the redesign, it took them an average of 1.2 seconds.
Pattern recognition
When asked what would make them faster and more confident, users wanted to see which transactions were late, risky, or unusually large, and what patterns were emerging over time. This shifted my focus to build a dashboard that’s easy to scan, highlights outliers, and still lets them drill into the details when something looks off.
I experimented with different ways of visualizing financial data, testing what worked and didn’t work with financial officers and others on the team.
Starting this project, I assumed the solution was simply letting users drill down into data. But as I looked closer, I realized the real challenge was deciding which data needed a detailed view and which was better as a summary.