Push Banks on Tech, Question Tax and Legal Blocks on Liquidity
Here are a couple of compelling concepts distilled by NeuGroup Founder and CEO Joseph Neu from his conversations and connections with members in the first two months of 2026:
Sharing bank technology. Treasury should no longer accept that cash management banks cannot offer superior, near real-time solutions (and made available outside their own bank’s systems), given all the time and money financial institutions have invested in technology. Banks need to be pressed to move faster with making their new tech available. They also need to give up on differentiating themselves from competitors based on proprietary tools kept in their own gardens, rather than enabling these new solutions to function across the global banking ecosystem. - When they relent, there will be no reason that cash and liquidity management can’t be automated. That will allow finance leaders to either reassign staff working in related treasury operations or to reduce headcount. There also may be a shift in people away from tech companies investing in automation solutions (including AI) to scale with fewer FTEs, toward more traditional companies that still do things the old-fashioned way (for now).
- If banks open up their technology, the infrastructure will be more like table stakes for bank selection—and differentiation will come from the people who enable its use and advise on the best ways to do it.
Refining global liquidity management. Broad consensus is emerging that more is possible to refine global liquidity management infrastructure than many practitioners realize. This requires treasury leaders to be willing to push back on legal and tax blocks by researching what is actually possible rather than assuming other functions—whose interpretations of rules and regulations often skew conservative—know all the answers.
- A key area where this need for action comes into play: cash trapped in countries including Vietnam, Turkey and India. These and other countries represent fertile ground where comparing notes with other treasury teams, conferring across banks and regulators, can yield new possibilities or validate what practitioners learn from doing their own research.
- A detailed mapping of global liquidity management infrastructure with a trusted group of peers can produce insight into maximizing efficiency from both a cash utilization and tax perspective.
- Among the benefits are uncovering concrete answers to liquidity plumbing questions like, “If they can do it this way, why can’t we?” It may well turn out that you can.
AI in IA: Using Petual To Simplify SOX
Members in recent discussions within NeuGroup for Internal Audit Executives said internal audit teams are beginning to move from debating AI agents to actively piloting them, with control testing emerging as a leading use case. Several members pointed to compliance with the Sarbanes-Oxley Act (SOX) as a natural starting point, given the repetitive, evidence-heavy nature of testing and the pressure to expand audit coverage without increasing costs. One agentic tool drawing interest is Petual, which nearly a dozen members said they are beginning to pilot. Audit executives said they are increasingly looking beyond general-purpose generative AI tools such as ChatGPT—which they noted can be too inconsistent for audit conclusions—and instead evaluating platforms built around structured agents designed to execute testing steps reliably and repeatably.
How Petual’s AI works. Petual’s platform can ingest unstructured audit materials including screenshots, emails and even spreadsheets with hidden tabs, links the right pieces of evidence to each audit item being checked, runs the specific audit procedures designed to gather evidence about accuracy and completeness and creates clear work papers with results, notes and explanations. - In a recent presentation to the group, Petual CEO Snir Kodesh said the intent behind the tool is to automate repetitive work while leveraging human judgment where it matters most: managing risk.
One member framed the broader shift as a productivity play rather than a staffing one, noting that AI is “not replacing heads, but it’s making each individual existing head 10x more productive so that we can fulfill that resource gap.” —Mehdi Hadi 
