Improving treasury operations comes down to three things: automating manual work, getting better visibility into your cash, and simplifying your banking structure. Most treasury teams still spend a disproportionate amount of time on reconciliation, spreadsheet-based forecasting, and managing too many bank accounts. Each of those areas has a clear path to improvement, and the gains compound when you tackle them together.
Automate Reconciliation and Cash Movement
Payment reconciliation, the process of matching incoming payments to open invoices, is one of the most labor-intensive tasks in treasury. Automation tools now handle near real-time posting of payments to invoices, drastically reducing the hours your team spends chasing mismatches. If your team is still pulling bank statements into spreadsheets each morning, this is the single highest-impact change you can make.
Beyond reconciliation, automated sweeping moves cash between accounts on a just-in-time basis, so idle balances in subsidiary accounts get deployed where they’re needed without anyone initiating a manual transfer. This is especially valuable for organizations with multiple entities or currencies. Companies operating across borders can also automate cross-border reconciliation through API integrations between their treasury management system (TMS) and banking partners, giving them real-time visibility into their global cash position instead of waiting for end-of-day reports.
The practical starting point is connecting your TMS to your ERP system and your banks through APIs, which are direct data connections that let systems exchange information automatically. If your current TMS doesn’t support API connectivity with your banks, that gap alone may justify an upgrade. Once the plumbing is in place, you can layer on more sophisticated tools like AI-driven transaction analysis and risk monitoring.
Sharpen Your Cash Forecasting
Inaccurate cash forecasts force treasury teams into expensive workarounds: drawing on credit lines they don’t actually need, holding excessive buffers, or scrambling to cover shortfalls. Improving forecast accuracy directly reduces borrowing costs and frees up working capital.
The most effective forecasting approaches combine a centralized data source with the right methodology for different time horizons. HP, for example, uses two distinct methods: an indirect approach tied to the company’s profit-and-loss projections for the first two months of each quarter, then switches to direct forecasting that tracks outstanding invoices, payroll, taxes, and other specific cash movements as the quarter progresses. This dual approach plays to the strengths of each method, using broader financial planning data when precision isn’t yet possible and granular transaction data when it is.
Getting your data into one place matters as much as the forecasting model itself. Expedia built a centralized data lake, a single repository consolidating financial data from multiple sources, and paired it with machine learning models. The result: forecast variances as low as one to two percent on projections extending one to two months out. You don’t need Expedia’s scale to benefit from this principle. Even implementing basic automated tracking of weekly collections, accounts payable, payroll, and interest payments gives you real-time insight into where cash actually stands, which is a significant upgrade over monthly manual compilations.
Machine learning and AI tools accelerate this further by analyzing large volumes of transaction data and identifying patterns that human analysts would miss, like seasonal payment behavior from specific customers or predictable delays in certain payment corridors.
Consolidate Your Bank Accounts
Many companies accumulate bank accounts over time through acquisitions, geographic expansion, or one-off needs that never got cleaned up. Each account carries fees, requires reconciliation, and fragments your cash visibility. Bank account rationalization, the process of closing unnecessary accounts and consolidating cash into fewer relationships, reduces costs and simplifies daily operations.
Virtual accounts have made this significantly easier. A virtual account structure lets you maintain a single physical bank account while creating an accounting sub-ledger that mirrors whatever reporting breakdowns you need by entity, currency, region, or purpose. You get the same visibility you had with dozens of physical accounts, but your actual cash sits in one consolidated position. This inherently improves funding efficiency because you’re not leaving idle balances scattered across accounts in different banks.
That said, some local accounts remain necessary. Certain markets require tax payments from in-country accounts, and significant transaction volume in a particular currency may justify direct market access. The goal isn’t zero accounts. It’s eliminating every account that exists only out of inertia. Physical cash concentration (automated daily sweeps that centralize excess cash and replenish accounts that need funding) pairs well with virtual accounts by reducing the intermediary accounts traditionally needed to move cash between subsidiaries and the treasury center.
Strengthen Payment Controls
Treasury fraud, particularly business email compromise schemes targeting payment teams, remains a persistent threat. Improving operations means building controls that catch fraudulent payment requests before money leaves the account.
Start with segregation of duties: the person who initiates a payment should never be the same person who approves it. Layer on multi-factor authentication for anyone with access to payment systems, and implement callback verification for any payment instruction that changes beneficiary details, especially wire transfers. Your TMS should flag anomalies automatically: payments to new accounts, amounts outside normal ranges, or instructions that arrive outside typical business patterns.
AI tools are increasingly useful here too, analyzing transaction patterns in real time and flagging outliers before they’re executed. The combination of human approval workflows and automated anomaly detection creates overlapping layers of protection that are much harder to circumvent than either approach alone.
Measure What Matters
You can’t improve what you don’t track. The Association of Corporate Treasurers identifies several KPIs that give treasury teams a clear picture of operational health:
- Cash visibility percentage: What share of your total global cash can you see in real time? If it’s below 90%, your data infrastructure needs work.
- First-time payment success rate: The percentage of outgoing payments that process without rejection or repair. Failed payments create rework and can damage supplier relationships.
- Forecast error by business unit: Breaking forecast accuracy down by unit reveals where your data inputs are weakest and where you should focus improvement efforts.
- Funding buffer: How much excess liquidity you’re holding beyond what’s needed. Too large a buffer means idle cash that could be earning returns or paying down debt.
- Cost of funds: What you’re actually paying for your borrowing relative to benchmarks. This tracks whether your funding strategy is competitive.
- Deal confirmation time: How long it takes to confirm FX trades, money market deals, or other transactions. Slow confirmations introduce settlement risk.
Review these metrics monthly rather than quarterly. Treasury conditions change fast, and a monthly rhythm gives you enough data points to spot trends without overwhelming your team with reporting overhead. Set targets for each KPI, share them with the broader finance organization, and use them to build the business case when you need budget for technology upgrades or additional headcount.
Sequence Your Improvements
Trying to overhaul everything at once usually stalls. A practical sequence: start with bank account rationalization because it simplifies everything downstream. Fewer accounts means less reconciliation, cleaner data, and better cash visibility. Next, automate reconciliation and cash movement, which frees up the team hours you’ll need for the more complex work of improving forecasting models. Finally, layer on AI-driven forecasting and advanced analytics once your data foundation is solid.
Each step builds on the last. Consolidating accounts gives you cleaner data. Cleaner data makes automation more effective. Better automation gives your team time to focus on strategic analysis instead of data entry. The compounding effect is real: organizations that follow this progression typically see measurable improvement in forecast accuracy, lower banking fees, and faster close cycles within the first year.