At the heart of the ongoing digital transformation in financial services is a powerful convergence of artificial intelligence (AI) and interoperability. These two forces are radically reshaping the financial sector, driving productivity, automation, and seamless workflows. The London Stock Exchange Group (LSEG) recently hosted a thought-provoking event where industry leaders and technology experts gathered to discuss the significant role AI and interoperability play in modernising financial services. The panels tackled how these technological advancements are shaping the future of finance, as well as the challenges and opportunities for innovation in this rapidly evolving landscape.
One of the main highlights of the event was how AI is revolutionising productivity within the financial services industry. Financial institutions are increasingly adopting AI-driven solutions to streamline operations, make real-time data-driven decisions, and boost overall efficiency. AI algorithms are processing vast amounts of data in seconds, making once labour-intensive tasks faster and more accurate.
In this regard, AI is particularly effective in areas such as risk management, fraud detection, and predictive analytics. BNP, for instance, specifically mentioned the utilisation of AI for surveillance but highlighted some issues with RAG (Retrieval-Augmented Generation) as part of this process. While BNY has developed an AI hub-and-spoke model (Eliza), combining the best of different GPT models to create bespoke AI solutions tailored for specific use cases, worthy of note as they have achieved 90% accuracy in predicting Treasury settlement failures.
However, the panel also emphasised that while AI is a powerful tool, its efficacy is tied directly to the quality of the data it processes. As one expert noted, “AI is true if the data is true,” highlighting the critical importance of clean, structured data to maximise AI’s impact.
The event also delved into how new technologies are driving automation and making workflows more efficient. The financial services industry is awash with data from multiple providers, presenting a significant challenge when it comes to managing and analysing it. On average, financial professionals use up to 25 different applications and over 100 data sources daily, making workflow management complex and prone to inefficiencies. Microsoft suggested the concept of a GenUX, perhaps proposing that a core UI becomes less relevant and omnichannel offerings become more relevant. ipushpull’s omnichannel integration into multiple chat platforms, in conjunction with Chat Relay communication between platforms and syntax-enabled chatbots, supports this direction of travel.
Moreover, through AI and automation, firms can now streamline workflow processes by integrating multiple systems and reducing the need for manual data entry. For instance, automation allows junior bankers to focus on higher-value tasks, reducing operational toil and freeing up time for more strategic work. HSBC, in particular, highlighted how the bank's version of GPT-driven AI has been tailored to enhance user experience by employing tools like sentiment analysis and personalised solutions, with a "human-in-the-loop" approach to ensure AI complements rather than replaces human judgment.
Therefore, workflow automation platforms, such as ipushpull, are proving to be vital in managing structured and unstructured data, allowing firms to connect disparate sources of information into a unified ecosystem. With such tools, data transformation can happen seamlessly, reducing the time spent on manually processing data from clients, and ensuring firms can react quickly to market opportunities.
Interoperability has emerged as one of the most crucial aspects of future-proofing the financial industry. As firms increasingly adopt AI, they face the challenge of integrating these new tools with existing systems, many of which are still reliant on legacy infrastructure. Interoperability, or the ability of different systems and applications to communicate and work together, is essential for creating a seamless user experience.
Financial firms are now striving for a "common UI" (user interface) where different systems can interact effortlessly, reducing the number of interfaces professionals need to use daily. It was highlighted that by integrating micro-applications and platforms, such as Microsoft Teams, Copilot, and LSEG Workspace, firms can build immersive, interoperable ecosystems that enhance both productivity and collaboration. Meanwhile, ipushpull has additionally integrated into multiple chat platforms to expand this principle.
Separately, a prime example of interoperability in action is FINOS and FDC3, which provide open standards to enable smoother data transfer between applications. These foundational layers allow financial institutions to connect AI tools more efficiently, enabling better decision-making through connected data ecosystems. The future of finance hinges on this ability to create seamless interaction between different AI models, ensuring a cohesive flow of information across the board.
One of the challenges financial institutions face in implementing AI and interoperability is the lack of standardisation across data formats. Although certain markets or processes have achieved a degree of standardisation, allowing natural language parsing and machine learning (ML) systems to facilitate trade negotiations or confirmations, there is still much to be done to ensure the smooth exchange of unstructured data.
Companies like ipushpull are working closely with the financial services sector to develop solutions that allow firms to manage complex data types with ease. By automating the transformation of data into normalised and standardised formats, they help firms develop efficiency, and productivity and support agility in a fast-paced market.
In addition to data standardisation, governance remains a significant consideration. As the industry increasingly integrates AI into daily operations, firms must navigate the regulatory landscape, ensuring that AI tools and models comply with industry standards. Governance and AI ethics will play a pivotal role in how firms implement AI, particularly in areas like surveillance and risk management, where transparency and accountability are paramount, and educating the wider workforce will be key.
Generative AI models, such as OpenAI’s Chat GPT, or in-house adaptations, are poised to play a transformative role in the financial industry. The event discussed how financial institutions are experimenting with these models to address specific challenges, such as the friction created by regulated entities or the vast amounts of unstructured data that need to be analysed. In an AI-driven world, the ability of generative AI models to interoperate with each other will also be crucial. As different AI tools begin to communicate more effectively, firms will be able to harness the full power of AI, whether for enhancing customer service, predicting market trends, or optimising internal processes.
In the parting message from the event, industry experts reassured participants that it’s never too late to embrace AI and interoperability. Financial technology has made exponential leaps over the past decade, and while some may feel they have "missed the boat," the reality is that innovation in finance is just beginning. The analogy was that the technology industry felt they were behind the curve in 2014 whereas the last 10 years have shown, “you are not late.”
As we look to the future, the importance of AI, data interoperability, and automation in financial services will only grow. With platforms like ipushpull leading the way in real time data sharing and workflow management and AI models evolving to address new challenges, firms that adapt and adopt will position themselves to thrive in an increasingly digital world.
Final Thought: The finance industry is at a crossroads. Those who harness the power of AI, and embrace the standards needed for interoperability, will be best positioned to succeed in this new era of finance. As technological advances accelerate, the industry must remain agile, focusing on governance, data quality, and user-centric solutions to fully realise the benefits of AI and interoperability. If you would like to know more about this topic or discuss your own requirements please get in touch.