Looking ahead to the future of automation, chat strategies present a tangible way to automate across the trading lifecycle, claims recent whitepaper from live data sharing and workflow automation services provider, ipushpull.
The whitepaper includes insights from senior individuals at firms including Kepler Cheuvreux, Microsoft, and SIX around how their own chat strategies are evolving.
Delving into the empirical application, these experts highlighted four key areas, firstly pinpointing price discovery as a main area where chatbots could be employed – to drive automatic and personalised prices directly to client chats.
In addition, when it comes to pre-trade negotiation, the whitepaper claims that chatbots could work to define potential trade details related to price, quantity, delivery terms and settlement procedures.
The fact that key details could be shared directly in chats also presents an opportunity for easy storage of data for compliance needs, says ipushpull.
Furthermore, in regards to post-trade, the fact that electronic confirmations could be sent directly via chat could avoid the potential for manual input errors, as well as improving compliance audit.
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Matthew Cheung, chief executive of ipushpull, asserted that how institutions interact, trade and report can be fundamentally changed through chat functionality, explaining: “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.”
Looking further down the line, the paper claims that the likely future of chat strategy is for AI assistants to operate within these chat platforms – even activated by voice, explaining that the key for this hinges on interoperability.
Looking at the current landscape, the report highlighted three types of chatbots which are integral to facilitating better life-cycle of trades: on-demand bots (activated via command line prompts to fetch data and prices), capture bots (those which capture relevant information from a chat and actioning, such as into an internal OMS), and LLM chatbots (with the potential to be trained to execute a range of complex tasks and workflows).
“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,” said Cheung.