Can you share how T. Rowe has been working to better integrate data and analytics within trader workflows?

“At T. Rowe we’ve been working on a multi-year project to integrate data and analytics within trader workflows as close as possible to the point of execution. The goal is to deploy trained models via API and to integrate this within our newest trading platform. Right now, our focus is on using predictive analytics and machine learning to determine the optimal number of counterparties to select for RFQ trades, and which to put in competition. Effectively we’re investigating all the underlying trade characteristics; currency pair, trade size, ease of liquidity access etc and marrying these together with our historical trading results as input data to design a model that will automate both pieces.

“We’re working to integrate data and analytics within trader workflows as close as possible to the point of execution” – Eric

“We’re at the information gathering stage currently and have multiple meetings with folks across the street to talk about how they’ve approached projects like this in the past, whilst making use of various whitepapers and external resources to build our knowledge.

We’re lucky to have the expertise internally with our trading analytics team to drive this forward and it’s exciting to be adding the next piece of the puzzle to the TCA and trading infrastructure we’ve built over the past four years.” – Brendan

It’s often difficult to communicate benefits and get buy in from varying internal teams for large projects like this – how have you approached the challenge?

“A large part of the project has been spent working closely with the trading team to get them to buy into and believe in what we’re doing. When we first began building out our TCA offering…