AI in Treasury: Beyond Automation to Smarter Decisions
The Future of AI in Treasury Is Better Decisions, Not Just Faster Processes.
Treasury management systems have long focused on making financial operations more efficient by automating previously manual tasks. Connecting bank accounts, consolidating data from multiple, diverse spreadsheets, automating reconciliations and generating reports have all helped treasury teams to reduce manual work and gain greater visibility into their financial positions. More recently, AI has introduced another wave of automation, and much of this has followed a familiar pattern of reducing manual labor and speeding up processes.
As a result, a major selling point for TMS providers – one which I experienced multiple times as a treasury practitioner – is that the TMS enables treasury teams to focus on more strategic initiatives. That’s all well and good, but freeing up that time can only go so far in improving business outcomes, unless you have deep insights.
This is why the biggest opportunities for AI in treasury go way beyond productivity. Federating multiple, complex, data sources, enabling continuous analysis, and giving treasury teams deep insights into their financial information gives AI the potential to make financial decision-making fundamentally smarter and deliver real financial improvements.
The direction we're taking with AI at xfolio goes beyond simple process improvement, and it’s a key driver behind our latest development: an MCP server that opens our treasury management platform to external AI applications and lays the foundation for a new generation of intelligent treasury agents.
Making treasury data more accessible and useful
One of the ongoing challenges in treasury management is that the information needed to make a decision often exists across multiple systems. Bringing together bank accounts, transactions, forecasts and financial records in a useful format still requires considerable effort. Modern treasury management systems have made significant progress in consolidating this information, but the next step is to change how people interact with it.
This is where Model Context Protocol (MCP) comes in. MCP enables AI applications to connect with external systems and access their capabilities through a consistent interface. xfolio's new MCP server enables customers to connect their own AI tools, such as Claude, Gemini or ChatGPT, to their treasury data within xfolio.
A treasurer could ask their Claude instance to identify accounts with the highest transaction volumes, examine cash balances or generate a report based on current financial information. The assistant can retrieve the relevant data from xfolio and present it in the format the user needs, potentially combining it with other information available in their AI environment. As this access is governed by xfolio's existing authentication and permissions framework, organizations retain control over which users can connect and what information they can access, including the ability to establish restricted roles for specific AI applications.
From answering questions to identifying opportunities
While MCP provides a new way to interact with treasury information, its greater significance is what that level of accessibility makes possible. For example, a treasury team managing liquidity across multiple bank accounts, currencies and business entities can get a consolidated view of cash positions through traditional reporting, and standard automation can ensure those reports are produced regularly and accurately. Both are valuable capabilities, but the treasurer must still interpret the information, investigate potential issues and determine the appropriate course of action.
AI creates an opportunity to make a more dynamic process, because instead of just presenting balances, a smarter system can analyse how liquidity positions are changing, compare them with expected cash flows, and identify areas that warrant closer attention. It might highlight an emerging funding requirement, identify cash that could be deployed more effectively or flag an unexpected pattern in transaction activity. This additional value comes from connecting information with context, and presenting potential actions in a timely manner.
For example, identifying excess cash is useful, but understanding how much of it could be deployed without compromising anticipated liquidity requirements, and what alternatives might be available, is considerably more valuable.
We’re not suggesting that AI could replace experienced treasury pros. These decisions still need treasury expertise, an understanding of the business’ priorities and appropriate oversight. However, AI can support that judgment by making the underlying analysis more comprehensive, timely and accessible. This ability to continuously evaluate financial information will become one of the most significant contributions AI makes to treasury management.
The next evolution: AI agents for treasury
Most AI assistants operate in response to a user's request. You ask a question, it retrieves information, and generates an answer. AI agents go beyond this interaction by carrying out a defined sequence of activities according to instructions, available tools and established permissions. In treasury, an agent can be configured to run recurring analyses, monitor changing conditions and surface information, without requiring someone to initiate every step.
This could take a number of forms. For example, you can begin the working day with an updated cash forecast that already incorporates the latest available information, alongside an assessment of significant changes and potential implications for liquidity. Alternatively, you can configure a system that continuously monitors cash positions and alerts a treasury team when expected funding needs or available liquidity move beyond defined thresholds.
The immediate benefit is that treasury professionals spend less time assembling information. The greater benefit is that they can respond earlier, evaluate more options and make decisions based on a more current understanding of their financial position.
Obviously this requires more than just connecting your AI tool to your financial data. Effective agents need clearly defined responsibilities, reliable access to relevant systems, appropriate permissions and boundaries around the actions they can perform. They also need to operate within a framework of human accountability – there’s a significant difference between an agent identifying an opportunity to move funds and an agent being authorized to execute that movement. Designing for these requirements is essential if AI is to become a trusted part of treasury operations rather than simply another interface for accessing information.
Building the foundation for intelligent treasury
As the only TMS designed from the ground up with AI at its heart, xfolio allows users to navigate the application, query financial data and create customised views through natural-language interactions. Our MCP server extends these capabilities beyond the xfolio interface, allowing our customers to work with their treasury information through external AI applications and incorporate it into their own workflows. It's also an important step toward our broader vision for agentic treasury management.
We see a future where specialised AI agents can take on defined treasury functions, analyse financial information continuously and help teams identify opportunities for better cash and liquidity management. Our approach is to build these capabilities around practical treasury use cases, with the underlying connectivity, controls and data access needed to make them genuinely useful.
As AI applications become more capable, the quality, accessibility and relevance of the information they can work with will play an increasingly important role in determining the value they deliver, and MCP is a key enabler of this. Treasury systems already hold much of the information organisations need to make better financial decisions, and the opportunity now is to make that intelligence available more readily, interpret it more effectively and use it to support action.
Ultimately, the success of AI in treasury shouldn’t be measured primarily by the number of tasks it automates or the hours it saves. The more meaningful measure will be whether organisations can make better financial decisions, identify opportunities earlier and generate greater value from the cash they manage.