BNP Paribas has implemented PPQ (Pushpull Quotes) from London-based workflow and automation specialist ipushpull, to streamline workflows around non-standard, complex trades for asset manager clients.
PPQ is a pre-trade workflow tool that standardises and automates the negotiation process between buy and sell side through a set of integrated data sharing and data-driven tools, using financial networks like Symphony and standardised syntax within private bilateral chats. BNP Paribas has implemented the solution for its LDI and Rates business.
“We’re seeing an increasing trend of financial institutions who want to have a streamlined and optimised workflow, to give them better alignment with their clients so they can deliver a better client service,” says ipushpull CEO Matthew Cheung. “LDIs can be quite complex in terms of structures and products,” He says. “Streamlining the sales person’s workflow means they can handle more inquiries and do more business instead of manually typing chat messages and updating spreadsheets and sending them back and forth.”
The standardised PPQ syntax allows chatbots to interpret key data within messages, display them within a custom application, and drive the workflow from a single screen. The inclusion of structured data objects within messages, containing instrument definitions, event descriptions and other metadata, further aids automation of pre-trade workflow.
“Essentially you have a human-readable message, with a machine-readable message under the hood containing metadata related to that client and their pre-trade workflow,” says Cheung. “Users like BNP can take those messages and plug them straight into their internal systems. Whereas historically, the salesperson on the desk would have to manually source that information from an email or a chat, and copy/paste it into an internal system to get a price. This is much more efficient, and it’s all real-time.”
Since the Covid pandemic, firms have become much more interested in moving away from their historic ways of doing things, and there are various steps along that digital transformation route, says Cheung. “The first step is digitising the manual processes of file sharing and people typing into chat, and automating processes based on the machine-readable data. Then you can start bringing in some predictive analytics and machine learning based on the standardised messages going backwards and forwards. Ultimately, the trader and salesperson can be more detached from the admin side of doing the trades, and focus more on value-added activities.”