Editor's Note: For validated insights about AI use cases, check out NeuGroup's AI Workbench , a members-only resource on the NeuGroup for Technology Transformation platform. It catalogs more than 50 curated, anonymized examples from across the Network, spanning cash forecasting, collections, treasury operations, SOX testing and risk. Members can see what peers have built and share what they’ve done. Treasury teams are building their own automation tools with AI and aiming them at low-value tasks that gobble up hours such as daily reviews of bank statements, reconciliation chores and a variety of file wrangling. Treasury leaders with limited IT resources are empowering analysts responsible for that work to create automations, clearing capacity for higher-value endeavors.
- At a recent virtual session of NeuGroup for Tech Treasurers , members walked through a number of solutions their teams have put into production, including agents that reconcile FX trades, review letters of credit and chase broken bank feeds—built with Cursor , Copilot , n8n and other approved software, no programming experience necessary.
- “If we didn't have an AI tool, there was no way we were going to hire IT personnel at 100 or more bucks an hour to go build some solution when offshore labor is able to do it,” one member said. “AI opens up stuff where actually there's an ROI now.”
Empowering analysts. Two years ago, one member's team asked IT to automate a task that took two to three hours every week, matching roughly 3,000 FX trades against statements from nine banks, each arriving in its own format. The problem proved too broad for the tech team to solve, but this year, an analyst with no previous computer programming experience used the AI coding assistant Cursor to generate a Python script that matches the trades automatically. The job now takes 15 minutes.
- The analyst ran the code past Gemini to validate it, then backtested against historical bank files to confirm accuracy. To satisfy auditors, the script feeds a familiar Excel workbook with one tab per bank, including timestamps and sign-offs. Designing this SOX-compliant output took longer than writing the initial program in Python.
- The treasurer said they deliberately built the final product in Python and Excel rather than keeping the workflow living inside Cursor or any AI tool, “because everybody's in the exploration stage. What if later on, the company decides that we can't use Cursor anymore? If we fully rely on an AI tool, then we might have a difficult time switching to something else.”
Retiring a daily slog. Another member’s team automated a ritual no one will mourn: Every morning, a team member scanned a bank-balance feed into Kyriba for missing bank account balances, then checked country holiday calendars to determine whether each gap was legitimate or a broken feed.
- An agent built on n8n—a visual workflow orchestrator that one member called "RPA on steroids"—now finds the email, runs checks and even drafts outreach to banks when it verifies a feed has failed.
- But until the agent proves itself, treasury confirms the fail before emailing the bank. The team learned this caution from a previous effort to build a bank group agent using Copilot connected to a SharePoint data lake. Like other members pursuing agentic treasury , they found the agent is only as accurate as the files underneath it.
After low-value automation, high-value addition. Following efforts to eliminate manual tasks, one member's team trained Claude on four years of share buyback data to build a scorecard that grades each bank by performance against VWAP benchmarks and accumulated savings. The scorecard can be generated on demand and is shared with the CFO. - Before AI, the member said each scorecard would have required using an IT resource, and the result was static, impossible to refresh the following month without repeating the whole exercise. Now the report regenerates at the press of a button.
- The shift is helping develop skills, not just saving time. Freed from updating the data, the team member has been creating new bank KPIs, including win rates by day of the week and performance in volatile stretches—and several of his additions are now permanent metrics.
- The scorecard is also becoming leverage: The member shares the results with banks, showing underperformers where competitors materially beat them—and treasury plans to replicate the approach for FX trading next.
