3 lessons in financial services AI transformation from the LSEG-Microsoft partnership
When LSEG (London Stock Exchange Group) and Microsoft announced our strategic partnership in December 2022, our shared goal was to create new value for LSEG customers by innovating on next-generation data, analytics, and workflow experiences. Along the way, we have aimed to reshape the future of global finance through joint innovation.
Microsoft Cloud for Financial Services
Unlock business value and deepen customer relationships in the era of AI
Today, 18 months into the 10-year partnership, we’re seeing incredible progress in bringing that joint vision to reality—with generative AI playing a pivotal role.
As Matthew Kerner, Corporate Vice President of Microsoft Cloud for Industry, noted at the Sibos 2023 conference, the partnership will ultimately evolve the customer experience at-scale across global financial markets to deliver advanced, easily accessible financial data and actionable insights through empowered workflows.
In my previous blog post, I reviewed a set of principles that I recommend for successful AI adoption in financial services. Here, I’d like to share three essential lessons from our work with LSEG that are relevant to other financial services organizations, specifically on how to determine a good solution, strategy, and focus for AI innovations.
Generative AI: The next phase of digital transformation
On the day of the LSEG-Microsoft partnership announcement, barely two weeks had passed since the unveiling of ChatGPT by OpenAI. Generative AI had not yet captured the world’s imagination, although it soon would. While it may have seemed to some people to have arrived overnight, at LSEG and Microsoft it was the logical extension of technical advances that we’d been focused on for years.
The emergence of generative AI represents the next phase of digital transformation. A central aspect of the first phase involved instrumenting operations, processes, and products across scenarios and industries to gain visibility into how things were working (or not) and organizing that telemetry into ever more sophisticated data stores to glean valuable insights. Generative AI supercharges that capability with large language models (LLMs) that go far deeper in mining data at incredible speed, and conversational interfaces that let people interact using natural language. The result is a democratization of empowerment and insights at a level not seen since the advent of the internet.
This is good news for financial services organizations who have made bet-the-company investments on digital transformation. It means that they are well-positioned to capitalize on the foundational attributes of hyperscale cloud computing, including security, compliance, and assurance. From there, the factor that can help realize the greatest innovation with generative AI is a data strategy that ensures the right data is made available to the right LLMs, and ultimately only to the right people.
With this broad baseline, here are three important generative AI lessons from our work with LSEG thus far.
1. Choose the right AI solution for the right problem
Generative AI is so cool that it’s tempting to try to employ the full scope of its capabilities. Resist this temptation. Focus your attention on problems that actually need solving, as opposed to searching for ways to put AI to work.
Start with problems that burden your users—for example, laborious processes among people whose time is expensive—and work backwards. Examine the applications and environments in which people spend most of their time and consider how to optimize them. One thing we’ve learned is that integrating AI directly into existing experiences and seamlessly adding support to existing workflows is far more effective than trying to create new application destinations. If you can suddenly summarize a document in 30 seconds that previously required 10 minutes, you are certain to find value.
In the LSEG partnership, we’re focusing on lighting up experiences within existing Microsoft investments, notably Microsoft Teams, Microsoft Power Platform, and Microsoft Fabric. Among the early highlights of this approach is a new solution in the works to streamline meeting preparation for investment bankers, built directly into Teams.
We’re also working together to create custom chatbots and copilots within Teams to minimize switching to custom application environments, and answer questions such as, “Show me the P/E ratio of [company].”
2. Apply your strategy for data management, tenancy, and residency
The highly regulated nature of financial services means that system design and software architecture are uniquely complex. Different people within the same organization will often require different levels of access to key data under management, and those rules must be respected by an AI solution.
AI governance
Explore best practicesRather than developing new ways to handle data access and security for AI, the best approach is to build on top of existing solutions that deliver those capabilities to enact and implement the required access and regulatory controls for AI. If existing solutions are inadequate, then the key order of business is to upgrade. In other words, fix the foundations first, and then build your AI solutions upon them.
This enables you to understand the topology of what is happening where—such as, which actions occur in which tenant (for example, LSEG customer data versus the Microsoft Graph)—while assuring comprehensive security and compliance. It will also allow you to continue your existing practices around data residency, as well as those for high-availability and disaster recovery (HA/DR).
With LSEG, we’re focusing on innovations designed to evolve how customers gain value from their data to unlock new opportunities. This involves combining LSEG’s data and content sources in Microsoft Fabric and integrating them into the enterprise-wide data catalog and governance framework of Microsoft Purview.
“Together with Microsoft, we are empowering our customers by increasing productivity while offering greater efficiency, resilience, and scalability across all workflows, and equipping the industry with the right tools for the next generation of financial professionals. Our multi-discipline practice of data trust is integral to LSEG’s open ecosystem for financial services, built on the foundation of transparency, security, and integrity of information. It aims to deliver rigorous data quality and governance processes, scalable technology powered by Microsoft Fabric and Microsoft Purview, and the “responsible AI” principles.”
Satvinder Singh, Group Head of Data & Analytics at LSEG
3. Evolve towards greater customer-centricity
In the early stages of innovation, a common challenge is how to deal with an overabundance of interesting opportunities and ideas from a very broad set of stakeholders. With so many compelling options in front of us with AI, we realized we needed to sharpen our focus to prioritize decision-making based on potential value to the business.
It is important to resist the temptation to “boil the ocean” by trying to solve too many problems at once. Instead, identify a handful of use case scenarios that focus on benefiting the end customer in ways that measurably impact business goals. To achieve this, our teams developed a methodology involving scoring and ranking of potential initiatives to identify the most promising options. Then we surveyed LSEG’s end customers to help us better understand their needs and inform us on their preferences.
By combining rigorous customer discovery and a clear validation prioritization process, we were able to identify opportunities we might have otherwise missed—for example, recognizing an emerging set of personas sitting at the intersection of data and AI that we could expect to grow in value in coming years. Embracing a customer-centric approach also created a discipline to quickly test and invalidate hypotheses that would be shown to offer minimal customer value at unacceptable cost.
Looking ahead with LSEG and Microsoft
As we move forward in our partnership, LSEG will continue to move beyond delivering data-focused products to offering services that are built on the company’s expertise, data assets, and insights gleaned through AI. This will help solidify LSEG’s pole position in the marketplace as it delivers new solutions to drive financial stability, empower economies, and enable customers to create sustainable growth.
For every firm, there is a profound opportunity to reimagine financial services. We are excited to continue partnering with LSEG to deliver this value to customers and the industry at large.
Learn more
- Discover Microsoft Cloud for Financial Services.
- Learn more about Microsoft perspectives on the era of generative AI:
