AI for Treasury and Finance
This article is written by HedgeFlows Artificial Intelligence (AI) is reshaping industries across the board, and Treasury and Finance are no exceptions. With its ability to enhance decision-making, streamline processes, and unlock valuable insights, enterprise AI is quickly becoming an essential tool for industry leaders. HedgeFlows has recently hosted an expert panel discussion on AI in the Treasury and Finance space, and we wanted to share some key learnings and insights that could help executives understand and leverage AI effectively within their organisations. Why AI is Relevant to Treasury and Finance The rise of AI in Treasury and Finance is driven by its unique ability to process large volumes of data, identify complex patterns, and automate repetitive tasks. For treasury functions, AI offers solutions to problems like forecasting cash flow, managing risks, optimising FX hedging, and improving data quality. With market volatility and rising operational complexities, augmenting human capabilities with intelligent systems is no longer a luxury but a necessity for organisations aiming to stay competitive. During the panel, Alexis Besse, a Managing Director, Head of Quantitative Trading from Jefferies, highlighted the practical application of AI in prediction models for market movements, noting its current limitations and vast potential. Meanwhile, Neh Thaker, co-founder of HedgeFlows, suggested that AI’s revolutionary value lies in enabling finance teams to move beyond mundane tasks to become strategic enablers for their businesses. But where do you start? Here are actionable insights from the panel discussion to help you consider integrating AI into your treasury and finance functions. Key Benefits of AI in Treasury and Finance 1. Improved Decision-Making AI can analyse data sets at an unprecedented scale and speed, helping teams make faster, more informed decisions. Whether it’s providing insights into market risks or identifying patterns in transactional data, AI empowers treasurers with actionable intelligence. 2. Enhanced Process Automation From cash flow forecasting to transaction matching, AI helps automate routine and time-intensive manual tasks. This doesn’t just save time; it also reduces errors, boosts accuracy, and allows teams to focus on strategic projects. 3. Better Risk Management The ability to identify and manage risks is greatly enhanced with AI tools. For example, AI models can provide real-time alerts about FX exposure or sudden changes in market conditions, empowering treasury teams to act proactively. 4. Data Integration and Analysis AI helps aggregate and harmonise unstructured data from disparate sources, offering a single source of truth. For enterprise-scale organisations, this level of data integration is critical for accurate reporting, forecasting, and compliance. 5. Cost and Resource Optimisation By freeing up Finance teams from mundane, repetitive tasks, AI allows for significant cost savings while creating the space for more impactful, strategic work. This is a game-changer for resource-constrained teams. Practical AI Use Cases in Treasury and Finance 1. Cash Flow Forecasting AI can improve the accuracy of cash flow predictions by analysing historical data, identifying trends, and accounting for multiple variables. According to James Kelly, the former Group Treasurer at Pearson, and founder of YourTreasury.ai even simple automation of forecast validation and reconciliation can save significant time and improve reliability. 2. FX Risk Management AI can assist in scenario analysis for FX exposure, offering advanced insights that allow treasurers to fine-tune hedging strategies. While predicting currency movements with certainty remains challenging, AI tools can improve execution timing and identify periods of high volatility. 3. Data Cleaning and Reconciliation Manual data reconciliation is a time drain for many organisations. AI tools can spot anomalies, identify duplicates, and automate the integration of datasets from various business lines, improving data accuracy and usability. 4. Regulatory Compliance AI-powered systems are now capable of analysing regulatory documents, automating Know Your Customer (KYC) processes, and ensuring compliance with industry guidelines. This enables faster approval cycles without compromising on due diligence. 5. Large Language Models for Summarisation and Reporting Advanced tools like large language models (LLMs) can summarise earnings reports, generate briefing notes, and even assist in drafting board presentations. This can help finance teams translate complex data into clear narratives, saving hours of manual effort. Addressing Challenges and Risks While AI presents incredible opportunities, it is not without challenges. Here are some considerations for treasury and finance professionals venturing into AI: 1. Data Security and Privacy Corporate information often contains sensitive data. Ensuring that third-party AI tools comply with data privacy regulations and maintaining robust internal control measures is essential. Platforms like OpenAI’s ChatGPT and Anthropic’s Claude provide varying levels of data security that organisations should assess carefully. 2. AI Hallucinations and Reliability Large language models are powerful but occasionally generate inaccurate or nonsensical outputs. For mission-critical tasks, always validate AI-generated outputs to avoid misinformation. 3. Integration into Existing Systems Implementing AI effectively requires integration with existing ERPs, Treasury Management Systems (TMS), and data storage platforms. Organisations must invest time and resources into building seamless integrations. 4. Human Skills Development AI is not a replacement for human oversight. Teams must be upskilled to use AI tools strategically, understanding their outputs and ensuring that the technology complements, rather than replaces, human decision-making. How to Get Started with AI in Treasury and Finance Here are steps to begin your AI transformation in the Treasury function: 1. Identify Routine Manual Processes Evaluate day-to-day tasks that are repetitive, error-prone, or resource-intensive. Start by automating these with AI-powered tools to unlock immediate efficiency gains. 2. Experiment with Accessible AI Tools Use generative AI platforms like ChatGPT for tasks such as drafting reports or generating insights. For more complex financial applications, consider platforms tailored to your industry. 3. Start Small with Pilots Instead of attempting to overhaul entire processes, pick one or two specific use cases for your first pilot. Examples include automating reconciliations or enhancing market research. 4. Collaborate with Technology Partners Work with vendors like TMS providers or specialised fintech firms that understand the nuances of your needs. For instance, HedgeFlows offers integrations that simplify treasury processes while leveraging the power of AI. 5. Upskill Your Team Encourage teams to learn about data science and machine learning to…
Franklin
The Forecast That Collapsed in 12 Seconds—and the Structural Fix You Shouldn’t Wait to Make
This article is written by Treasury4 The cash model looked clean.13-week cash flow forecast? Built.Global liquidity dashboard? Refreshed.Intercompany schedules? Squared away.Board pre-read? Uploaded with time to spare. Then the CFO asked: “Do we actually have control over that entity?” Twelve seconds later, the model was in question.Forty minutes later, tax and legal were looped in.By the end of the day, the entire liquidity picture had shifted. No time to read? Take these takeaways with you: Missing or outdated ownership data breaks cash forecasts in real ways. And when the board’s watching, small gaps become big problems. This is how clean-looking forecasts go sideways Cash forecasts don’t break because of a formula error. They break when smart people are forced to build strategy on partial visibility—especially when it comes to ownership data. If you’re managing entity-level inflows, trapped cash, intercompany lending, or FX exposure, you know how easily one outdated assumption can throw the entire plan off course. When ownership data is missing, outdated, or siloed from treasury systems, you risk: These aren’t theoretical risks. They show up in real-world decisions, reforecasts, and uncomfortable conversations with leadership. Where ownership gaps quietly derail your cash reports Global Liquidity Reporting You show $32.5M in cash across entities—but $7.4M of that sits in a sub your company only owns 45% of. You can’t actually access it. What goes wrong: The CFO greenlights a $10M equity investment assuming it’s covered. Treasury scrambles to shift funding last-minute. 13-Week Cash Flow Forecast You include inflows from a LATAM entity assuming full control. But you only own 49%. What goes wrong: The forecast is overstated. Treasury plans around phantom cash. You spend the next week revising models and re-explaining. Intercompany Lending Capacity You model a loan between two entities—only to discover one is outside your control threshold. What goes wrong: The loan violates internal lending policy. Audit flags it. It has to be reversed. No one’s thrilled. FX Exposure Management You hedge your EUR exposure based on entity cash totals—without realizing two major subs are majority-owned by external partners. What goes wrong: You overhedge, misprice the risk, or underprotect the business—and treasury takes the hit. You often don’t know it’s broken until it’s already public These mistakes don’t show up with flashing red lights. They sneak into your dashboards, your PDFs, and your board decks—until someone asks a question that reveals the gap. Then it’s a scramble to explain. “Why does APAC show more cash than Ops is seeing?”“Why is the working capital forecast off from the liquidity report?”“Why does tax say we can’t move money out of that entity?” Sound familiar? Why this happens—even when you triple-check your work You’re not the problem. The system is. Here’s what you’re up against: This isn’t a data-entry issue. It’s a structure issue. And when ownership data isn’t linked to cash reporting, bad assumptions happen fast. Here’s what you can do in the near term to survive the next board meeting You might not have time to overhaul your systems before the next review, but you can reduce the risk of a public fumble. Start here: This won’t fix the root problem—but it’ll prevent you from walking into the next meeting with a blind spot you can’t explain. Beyond the bandaid: solving the real problems If you’re constantly cross-checking entity control, adjusting percentages manually, or explaining caveats on every cash forecast—you’re not inefficient. You’re under-equipped. Modern cash and treasury platforms solve this problem at the root by tying ownership data directly to how cash is modeled, reported, and managed. Here’s how they turn chaos into clarity: They turn your org chart into forecast logic These platforms ingest your legal structure and apply it directly to reports. Entities under full control are included in cash totals, minority stakes are weighted or clearly flagged, and entities without access are automatically excluded from deployable cash. They show controllable cash—not just balances Dashboards break out what’s actually accessible. You see trapped cash, minority-owned balances, and restrictions clearly—so you can plan with confidence. They automate ownership-aware forecasts Templates apply control logic across inflows, outflows, and intercompany positions. When ownership changes, your forecast adjusts automatically—reflecting the new structure without breaking the model. They sync legal, tax, treasury, and finance Everyone sees the same ownership data. No more pinging Legal. No more reconciling spreadsheets. Everyone works off one dynamic source of truth. They catch issues before they reach leadership If your model includes restricted cash or a non-controlling entity, the system flags it. That’s the difference between fixing quietly—and explaining publicly. Want to understand exactly how this all works? Let’s dive in. Fixing ownership logic and entity clarity is just the start. Once that foundation is in place, the real upside isn’t just accuracy—it’s velocity. Power users don’t just want to close the books faster. They want to analyze faster. Spot trends earlier. Answer high-stakes questions without needing to triple-check every input. That’s what a modern cash and treasury platform unlocks. Smarter Forecasting and Scenario Planning—Not Just Prettier Reports When ownership data, entity structure, and real-time balances are embedded into your architecture, you don’t have to rebuild forecasts every time something changes. You can start focusing on what happens next. With the right tools, forecasting moves beyond static reports and into: If your team already works in advanced analytics tools or cloud data platforms, a modern treasury solution can integrate with your existing stack—so you get scalable forecasting and reporting without having to rebuild your entire process. The shift is huge: from monthly forecast sprints to on-demand insight generation that finance and treasury can run without pinging three other teams. Trend Analysis That Tells You More Than “This Number Changed” Most platforms surface variances. That’s the easy part. What power users need is a clear story behind the movement—and the tools to investigate without friction. With structured treasury architecture built to support analysis—not just reporting—you get: This lets you answer questions like: “Why is this region always running a surplus we can’t move?”“Are collections lagging in specific entities—or is the forecast wrong?”“Should we…