“AI’s transformative potential so far exceeds that of most modern innovations that it may be better compared to the steam locomotive,” says Dilip Venkatachari, senior executive vice president and chief information and technology officer at U.S. Bancorp. It is a comparison drawn from decades spent working across analytics, financial systems, and digital infrastructure which are fields where he holds more than a dozen U.S. patents. The scale of what the bank has started rolling out is a series of AI-powered tools meant to change how corporate clients manage liquidity and how employees deliver technology inside one of the country’s largest financial institutions.
U.S. Bank Liquidity Manager, is a new forecasting and cash-visibility product developed with Kyriba which arrives at a moment when many finance teams are still stitching together spreadsheets, manual reconciliations, and multi-bank portals to understand something as fundamental as their daily cash position. For treasurers working across continents and currencies, that lack of visibility can create delays, uncertainty, and operational strain.
The bank’s answer is a platform that blends traditional forecasting techniques with advanced AI, allowing companies to analyze historical patterns, predict near-term cash flows, and run scenario planning as conditions change. The engine behind this capability is Cash AI, Kyriba’s forecasting layer that adapts to new information and continuously refines projections. Treasurers receive not just baseline forecasts but dynamic updates that reflect working-capital movements as they happen.
Liquidity Platform Designed for Fragmented Cash Environments
According to Kristy Carstensen, who leads Treasury and Payment Solutions at U.S. Bank, the core challenge the bank wanted to address was persistent fragmentation. “Many companies struggle to obtain a timely and accurate view of their liquidity, especially when managing multiple bank accounts across geographies and currencies,” she explains. Liquidity Manager is designed to centralize that work by connecting directly to accounts, automating reconciliations, and reporting across domestic and international banks.
Liquidity Manager introduces a suite of capabilities designed to give treasurers clearer control over their cash environment, beginning with Cash Forecasting powered by Cash AI, which analyzes historical cash flow patterns and adapts projections as new data emerges to deliver continuously updated forecasts. The platform automates daily cash positioning to provide real-time visibility by account, bank, region, and entity, while also managing and tracking zero-balance account sweeps across institutions to streamline ZBA cash pooling. Its multi-bank reporting feature consolidates balances and transactions from North American and global banks, replacing the need to navigate multiple banking portals. By centralizing forecasting, reconciliation, and reporting into a single system, Liquidity Manager reduces operational complexity and minimizes the cost and manual effort traditionally associated with managing liquidity across fragmented financial environments. Liquidity Manager allows them to consolidate reporting, automate routine tasks, and rely on a single platform to view cash, reconciling what previously required hours of manual work.
Integrated Into a Redesigned SinglePoint
The new tool is accessible through SinglePoint, U.S. Bank’s treasury management system used for initiation, reporting, and client oversight. In October 2025, the bank launched a new generation of the platform featuring enhanced dashboards, automation tools, and improved user flows. The redesign aims to reduce blind spots and manual dependencies that have historically slowed down daily treasury operations.
By housing Liquidity Manager inside SinglePoint, the bank brings forecasting, positioning, reporting, and workflow management into one place. The release of Liquidity Manager is not an isolated product update but part of a larger series of technology moves U.S. Bank has made in 2025.
In June, the bank expanded its Embedded Payment Solutions, adding a strengthened for-benefit-of (FBO) structure and real-time payments. These enhancements allow businesses to automate transactions, simplify onboarding flows, and track funds more precisely. For companies running marketplace or platform models, the improvements are intended to reduce friction in how money moves between users.
That same month, U.S. Bancorp announced a partnership with Fiserv to integrate its Elan Financial Services credit card program with Fiserv’s Credit Choice solution. The collaboration is aimed at supporting more efficient digital card issuance for financial institutions, bringing onboarding, activation, and funding into a streamlined experience.
Customer Service, Testing, and Software Development
Venkatachari offers an inside view into how the bank is introducing AI beyond its commercial treasury products. The bank has deployed AI in its customer contact centers, enhancing interactive voice response systems with personalization and giving agents faster support during live calls. These implementations have produced improvements in efficiency, call quality, and cost that are tangible outcomes for both customers and service teams.
Within the technology organization, AI is being applied across the software delivery pipeline. The bank is using testing automation tools and developing agentic software, a shift that Venkatachari says is helping reduce coding time and accelerate feature development while improving output quality. These tools are not intended to replace engineering teams but to amplify their efficiency and reliability.
The bank’s next phase, he notes, focuses on scaling AI responsibly across the enterprise, guided by governance and guardrails to ensure that adoption remains safe and compliant.
Lessons From Early Deployment
Rolling out these systems has surfaced lessons that influence how the bank approaches AI at scale. One of the earliest challenges was tool selection. As Venkatachari describes, the market is crowded and fast-moving, making it difficult to choose among solutions. After running several proofs of concept, the bank concluded that ease of integration outweighed the appeal of experimental cutting-edge features.
The second learning came from measurement. To know whether AI tools were actually improving workflows, the bank needed granular performance metrics, not broad indicators. Different teams were using tools in varying contexts, requiring the bank to track outcomes across code types, tasks, and functions. This push for detailed metrics is now a central part of how the U.S. Bank deploys new AI capabilities.
The third, and most significant, factor was change management. Venkatachari says the technology itself was not the hard part; the challenge was preparing people to work differently. The bank created safe experimentation environments, rolled out tools employees could use to solve immediate problems, and formed ambassador groups to share experiences across departments. These groups meet frequently and help staff build comfort with new processes.
Senior leadership involvement has been crucial. Employees, he notes, need to believe in results they cannot yet see, and consistent engagement from top leaders helps reinforce that encouragement.
U.S. Bank’s moves come as other major institutions are intensifying their own AI agendas, particularly in treasury and forecasting workflows. Citigroup has rolled out AI-enabled FX forecasting and deepened its digital-asset work with partnerships around institutional on- and off-ramps. Bank of America has reported record usage of its AI-powered CashPro tools and continues to expand the reach of Erica, its virtual assistant. The bank also launched CashPro Insights, a working-capital optimization platform that uses data intelligence to guide liquidity decisions.
These developments highlight how large financial institutions are reorganizing around real-time visibility, automated analysis, and AI-supported workflows which are trends that Liquidity Manager directly addresses for U.S. Bank’s client base.
For U.S. Bank, the introduction of Liquidity Manager is strengthening operational efficiency, expanding digital capabilities, and offering clients more reliable tools for financial decision-making. The bank expects these efforts to reduce costs, support revenue growth from enhanced product lines, and improve forecasting and cash management accuracy. For 2025, U.S. Bancorp has stated its expectation of positive operating leverage exceeding 200 basis points, marking an important financial target tied to its digital transformation. And for Venkatachari, AI is not being introduced as an isolated technology layer but as a capability woven into customer experiences, risk operations, and product development. “These are not projects that can be delegated and forgotten,” he says. “They require thoughtful senior-level engagement to help build success.”
