Transforming financial workflows with chatbots and generative AI
By Brendan Bradley
In this post, we explore the exciting potential of integrating chat platforms with generative AI to revolutionise capital markets workflows and lifecycle management. We discuss how this integration can improve response times, reduce manual workload, and enhance efficiency and productivity.
The idea of using chat platforms for price formation and negotiation, whether via RFQs or IoIs, subsequent affirmation and confirmation and post-trade settlement enquiries, is not new. However, the ability to query the consequent structured and unstructured data pool to create further efficiency and productivity has been minimal due to the “walled garden” platforms hiding the treasured information that lies within!
Of course, it is difficult to write about Chat at the moment without mentioning ChatGPT (other large language models are available!). Many financial institutions have built use cases leveraging Generative AI and open source models for testing and evaluation. And integrating Generative AI with chat platforms will enable seamless and interactive communication between traders and AI-powered chatbot assistants. This will improve response times, reduce manual workload, and further enhance the efficiency/productivity of capital markets workflows and lifecycle management.
In the meantime!
So, the future is bright! But where are we coming from? Despite changes in regulation and improvements in electronic trading, there remains a proliferation of voice- and chat-based communication. Therefore, within pre-trade negotiation and/or post-trade confirmation, market participants have adopted new models to harness the changing technology while preserving the advantages of traditional workflow in markets. This is particularly true when liquidity is fragmented or market participants are creating strategies and pricing in real-time, pulling live data from various sources and presenting complex strategies to their clients as single executable products.
The smartest among them are delivering a largely automated approach that replicates the core aspects of voice trading and chat while providing clients, both internal and external, with all of the advantages of electronic price formation, data collection and immediate information retrieval, and meeting Best Execution requirements. This requires an interoperable live data sharing service into omnichannel delivery of chat, combined with chatbots, to unbundle and re-bundle workflows that live in Excel spreadsheets, emails, and file shares. This produces a rich canvas within chat platforms combining the best parts of integration and collaboration tools into new structured workflows.
Where have you been all of my life?
Desktop real estate is limited and users don't want multiple applications open, having to switch between them as they access multiple services and venues. Allowing them to access services and venues from within their application of choice is the way forward. Omnichannel delivery by ipushpull provides a unified productivity layer combining chat and data to form new structured data-driven workflows. Where chat platforms provide real-time chat, ipushpull provides real-time data, shared across chat and other interoperable applications, platforms, or databases.
By taking the heavy lifting away from robotic tasks, such as copy/paste, rekeying of data etc; market participants can free up time for more value-added tasks such as cultivating relationships, generating ideas and creating revenue. Furthermore, by developing standardised syntax participants can rely on consistent formats, using configuration tools to customise and personalise their offering and low code/no code interfaces to automate for better efficiency and reduced risk.
This approach is transformative for those markets that are opaque, less liquid or have unique pockets of liquidity, think OTC markets, strategies, EFPs and block trades.
Chat with benefits – “reap what you sow!”
ipushpull has found that once market participants embrace these new data sharing and chat functionalities, the next step is to look for ways of harvesting the chat messages, revolutionising the way they gather information, analyse market sentiment, gain further insight and drive trading decisions. These conversations provide a unique window into the collective intelligence of market participants by leveraging natural language processing (NLP) and machine learning techniques.
Harvesting chat messages involves the collection, processing, and analysis of large amounts of textual data from various communication channels, such as chat rooms and messaging apps. NLP algorithms can categorize and extract relevant information from these messages, including discussions about specific securities, market trends, news events, and even rumours. By aggregating and analysing this data, market participants can uncover valuable patterns, sentiment shifts, and emerging trends, while fostering collaboration and knowledge-sharing to make more well-rounded decisions, and gain a broader perspective on market dynamics.
Another further benefit of this richer data includes monitoring of response times and competitiveness of quotes to provide better analysis of services and other useful management information.Currently this data is often just not available so ensuring it conforms to a common standard and sits in common communication space delivers transformational improvements.
Here's one we made earlier
ipushpull has already developed standardised syntax that filters information using chatbots and low code workflow tools, using dashboards and buttons to relay information quicker while facilitating the logging of audit and best execution requirements. As technology continues to advance, the future of harvesting chat messages holds even greater promise.
Integration with AI and machine learning algorithms will enable more accurate sentiment analysis, improved anomaly detection, and enhanced pattern recognition. Furthermore, the incorporation of data from multiple sources, such as news feeds and financial statements, will provide a comprehensive view of the market landscape.
This further helps ipushpull's customers to deliver the right data, to the right place at the right time, to enable users to collaborate and share insight in a multi-asset class/multi-workflow, omnichannel environment. For more information on our omnichannel data delivery solutions head over to this page.