As AI continues to advance, with more developments on the horizon, entire industries and business models are set to change significantly over the next decade. There are many dynamics at play, with the capabilities of AI outrunning the agility of many organizations. This is particularly the case with many financial institutions, as they continue to modernize legacy core infrastructure through cloud migration.
Of course, the first port of call for most FIs is to develop AI use cases that are abstracted almost entirely from legacy systems, such as customer support chatbots. When we look at how the contact center works, it’s an obvious place to start. Customers visit a website, call a customer support number, or send emails when they want to raise an issue. These interactions can be entirely routed through cloud solutions, with APIs connecting them to the relevant internal data repositories, such as customer relationship management (CRM) systems.
While the contact center use case is the most readily adopted AI solution for financial services firms, there are other quick wins that will enable employees to focus on more high-value tasks, particularly as organizations grow their AI understanding and engagement.
Quick wins for customers and employees
With so many services and applications delivered through the cloud, customers can also benefit from AI-driven enhancements and efficiency. Many financial products now have in-app AI assistants, with customers benefiting from increased accessibility through being able to interact with their applications through natural language. This leads to clear gains in user experience (UI) as well as user experience (UX).
Similarly, these assistants can also enhance internal workflows, with banking professionals able to search, discover, extract and summarize information. They also unlock the ability to create net new content, such as reports, from existing content. This results in employees across all business functions becoming more data-driven, with workflows enhanced by insights that would have been out of reach without AI tools.
Take the example of payments. Banks and payments providers deal with vast amounts of complex payments data, so the ability to analyze this data and retrieve insights through natural language is extremely useful.
Another example is training in complex fields, such as trade finance. This sector faces a significant talent gap as experienced staff come to the end of their careers or transition to other roles. With internal AI assistants, new team members can get up to speed much more quickly as they self-serve queries about processes and workflows through prompt-based assistance. As a result, bank employees no longer have to sift through extensive documentation to find the answers they need.
For more technical teams, such as developers, AI tools deliver incredible value. Code completion assistants are increasing developer speed and accelerating software development lifecycles, resulting in the rapid delivery of new updates and features for customers. Of course, not all employees within an organization are going to be as adept as technical teams when it comes to AI tools, but this is where technical leaders across financial institutions must drive the implementation of bespoke upskilling roadmaps for different business functions and teams.
Key investments for financial services organizations
Most significant AI efficiency gains in financial services relate to the automation of time-consuming, low-value work for professionals across all sectors and functions. Generative AI has been the driving force behind much of the adoption and integration we have seen in recent years and use cases range from transcription to translation and digitizing paper-based documents. For lending teams, for example, being able to digitize, query and manage large volumes of complex loan documentation at scale, and ensure downstream applications can benefit from this data, is transformational.
As nascent technologies and advanced capabilities take shape, existing investments will also benefit. The rise of AI agents, for example, is unlocking new avenues of innovation as agents can plug into generative AI tools. Chatbots enhanced by AI agents can deliver advanced knowledge and data search and discovery by connecting to different LLMs and approved external sources.
New protocols that allow agents and LLMs to communicate with one another are also extending what is possible with AI. The two key protocols that have emerged are Agent-to-Agent (A2A) and Model Context Protocols (MCP). As the name suggests, A2A protocols enable agents to communicate and collaborate with one another autonomously, precipitating the creation of more expansive and dynamic AI systems. MCP is a framework that gives LLMs the ability to access other tools and systems, such as APIs, external databases, and agents.
As we move toward the creation of fully agentic systems, investment in these protocols is essential for financial services organizations. By unlocking new and secure means of communication between AI agents, APIs and external data sources, AI-led innovation and collaboration is supercharged.
It is an exciting time for financial services, as AI is delivering stunning productivity gains for internal use cases and enhancing products and services across the ecosystem, from lending to capital markets.
All branches of financial services are rich in data, and data is the fuel that powers AI. This is why we are now seeing an explosion in the number of fintech and technology partners that specialize in AI offerings and enhancing financials services with advanced technology. The key hindrance for the industry is legacy technology, but collaboration with these partners, and the adoption of cloud services, is increasing agility and ensuring financial services firms are able to take advantage of the full power of AI.
