Chat functionality is changing how institutions interact, trade and report, according to Matthew Cheung, CEO of ipushpull.
“The advent of chatbots such as ChatGPT has opened firms’ eyes to the power of AI-enabled chatbots and have revealed the breadth of application in financial services – from interpreting user queries and providing relevant responses, to personalising information delivery, by incorporating opensource models for testing and evaluation,” he said.
ipushpull, a provider of live data sharing and workflow automation services, has published a white paper providing a comprehensive analysis of how chat strategies can be deployed to enhance institutional trading, as chatbots and AI play a growing role in facilitating interactions between counterparties and the lifecycle of a trade.
The paper’s key findings include the extent to which chat functionality is being used to autonomously extract, capture, standardise and interpret structured and unstructured data from a diverse range of sources. It also highlights the challenges institutions face in implementing cohesive chat strategies, expanding chatbot adoption and effectively integrating functionality to drive internal efficiencies and enhance client service.
For the white paper, ipushpull interviewed senior figures in financial services, tech and academia including Kepler Chevreux, Trayport, Symphony, Microsoft, UCL and SIX to gauge their view on how their chat strategies are evolving. The report identifies three types of chatbots that have become integral chat strategies that look to enhance the trading lifecycle:
- On-demand bots: Chatbots that are activated via command line prompts to fetch data and prices.
- Capture bots: Chatbots that capture relevant information from a chat, for example capturing trade information via chat and actioning into an internal OMS.
- LLM chatbots: Chatbots that can be trained to execute a range of complex tasks and workflows, such as extracting, transforming and aggregating complex data or producing sophisticated predictive analytics.
The report highlights how chat strategies can be deployed through the coordinated integration of chatbots across the trade lifecycle from pre- to post-trade and the current capabilities of chatbots:
- Price discovery: Chatbots are being used to drive the automatic, personalised of prices directly to client chats.
- Pre-trade negotiation: Chatbots are being used to define potential trade details such as price, quantity, delivery terms and settlement procedures.
- Trade capture, booking and reporting: Automatic capture of key details directly from chat conversations – storing data in standardised and original formats for compliance.
- Post-trade confirmations: Using electronic confirmations directly within chat minimises manual input errors and improves compliance audit.
The paper outlines the likely future of chat strategy: co-pilots. AI assistants within chat platforms will soon be able to automate an even greater range of workflows and can even be activated by voice. Key to securing this future will be ensuring interoperability between platforms and systems, and that tools are secure and compliant.
“Despite chatbot’s level of sophistication, we are still at an early stage in its adoption and development. Firms therefore need to understand how this technology can be leveraged and deployed as part of an overarching chat strategy. Those not already using or planning to use the technology risk ingraining inefficiencies and could miss out on trading opportunities, as chat becomes a go-to means of interacting and connecting with counterparties,” Cheung said.
