Case Study

Translating Treasury Signals into Actionable UX

Leveraging machine learning to automate treasury insights and reduce manual data wrangling.

Introduction

Bank of America's CashPro platform processes millions of corporate transactions every day. Each payment data point carries a stack of base?level signals that help to identify what dimension these data points are describing and communicating.

A data point, such as a payment, is comprised of a pattern of signals, and when aggregated, exposes cost leaks, timing inefficiencies, and fraud risk. CashPro Insights was conceived to analyse both layers: signals attached to raw data and the emergent patterns those signals create in aggregate.

Our design goal was clear: transform these patterns into plain?language recommendations that a CFO or Treasurer can absorb at a glance, yet still allow them to trace each recommendation back through insights, to the underlying signals, and down to the raw transactions if they desire full validation.

The result is a Financial Intelligence Dashboard that turns mountains of payment data into an embedded advisor, guiding corporate finance teams to optimize their payment mix, improve working capital, streamline cross?border flows, and reduce fraud exposure all within their existing CashPro workflow.

Additionally, a framework for scaling recommendations from the initial few to what could perhaps be thousands of ways a client could make tactical and strategic decisions that would result in operational efficiencies and cost savings.