Enhancing cash flow management with short-term financial projections

Cash flow management has become a frontline discipline again, not just a finance hygiene task. In the AFP 2025 Treasury Benchmarking Survey, nearly three-quarters of treasury practitioners cite cash management and forecasting as top priorities, and over 60% call cash/liquidity forecasting the most challenging activity. The message is clear: organizations want clearer, faster, and more reliable short-term visibility.

Short-term financial projections are one of the most practical levers to get there. When designed with the right horizon, data, and cadence, they help teams decide how much liquidity is truly available, when funding is needed, and how much cash can be invested safely, an important point given that safety dominates short-term investing decisions for 61% of organizations (AFP 2025 Liquidity Survey).

1) Why short-term projections are now a treasury priority

Treasury priorities are shifting from periodic reporting to continuous control. The AFP 2025 Treasury Benchmarking Survey reports that “cash management and forecasting” is a top priority for nearly three-quarters of practitioners, while more than 60% describe cash/liquidity forecasting as their most challenging task. This combination, high importance and high difficulty, is exactly where structured short-term projections add value.

Short-term forecasts also respond to a broader liquidity backdrop where funding conditions can change quickly. Even sovereign issuers model near-term cash balances and borrowing needs: the U.S. Treasury’s TBAC discussions (Nov 2024 refunding context) reference assumptions about cash balances and quarterly borrowing needs, underscoring that short-horizon liquidity planning matters even at the largest scale. Corporate treasuries operate with different instruments, but the same principle applies: short-term decisions depend on near-term cash reality.

Finally, the “payoff” is measurable. Working-capital performance data (Hackett via CFO.com, July 17, 2025) shows the average cash conversion cycle among the 1,000 largest U.S. nonfinancial public companies improved to 37 days in 2024 (from 38.3 in 2023), with days payable outstanding improving to 59 days (from 57.2). Short-term projections help convert working-capital initiatives into liquidity outcomes by translating operational changes into day-by-day cash impact.

2) Choosing forecast horizons that match real decisions

A common reason forecasts fail is a mismatch between horizon and decision. Treasury playbooks are increasingly codifying practical horizons (2026 cash-forecasting guide): a very short window (roughly 3 to 15 days) is often best handled daily using transaction-level bank activity and scheduled payments. This is the zone where timing accuracy matters most, payroll, taxes, settlements, and supplier runs.

For control and runway, the same guidance frames a medium-term horizon of roughly 4 to 13 weeks, refreshed weekly, integrating ERP signals like invoices, purchase orders, DSO assumptions, debt deadlines, and recurring items. Thirteen weeks is often described as a “sweet spot” because it’s long enough to see funding pressure forming, but short enough to remain actionable and not overly assumption-driven.

Separating horizons also improves accountability. Daily forecasting should be owned by teams closest to cash movements (bank activity, payments), while the 13-week view benefits from collaboration across FP&A, AR/AP, and procurement. Done well, this structure reduces the tendency to overload a single model with every use case, which usually leads to complexity without better accuracy.

3) Direct vs. indirect approaches: designing projections that hold up

Short-term projection design often starts with a methodological choice that’s also familiar in accounting. IAS 7 allows operating cash flows to be presented using either the direct method (major classes of gross receipts and payments) or the indirect method (profit adjusted for non-cash items and working-capital changes), and it explicitly encourages the direct method. While IAS 7 governs reporting, the conceptual distinction is useful for forecasting governance and model design.

For short-term liquidity visibility, the direct method is widely positioned as the most accurate approach. A current practitioner-oriented industry explainer notes that direct-method forecasting uses bank transactions, payables/receivables, and expense data, making it “highly accurate for short-term forecasting,” provided data discipline is strong. In practice, that accuracy is exactly what treasury needs when deciding whether to draw on a facility, delay discretionary spend, or execute an investment.

Indirect-style approaches can still be valuable, particularly for longer horizons and scenario work where drivers matter more than individual payments. But for near-term cash control, direct-method mechanics (what will hit the bank and when) create a clearer bridge between operational events and liquidity outcomes. Many organizations blend both: direct method for 0, 15 days and a driver-informed roll-forward for weeks 3, 13.

4) Data discipline: the real bottleneck behind “forecast accuracy”

Organizations increasingly recognize that forecasting problems are often data problems. In the PwC 2025 Global Treasury Survey, 76% cite poor data quality as a key pain point, and many still struggle with manual collection and consolidation of forecasting data (reported as 38% for very large firms in the survey commentary). If the input data is late, incomplete, or inconsistent, forecast “accuracy” becomes largely a measure of how fast the organization learns about surprises.

Short-term forecasts are especially sensitive to data latency and categorization. A single mis-timed payment run, an unposted bank fee, or an untagged intercompany transfer can distort the near-term picture. Building discipline means standardizing transaction tagging, defining a clear cash taxonomy (collections, payroll, taxes, capex, debt service), and setting rules for cutoffs, what must be included by what time each day or week.

It also means acknowledging that many organizations still rely on tooling that makes discipline hard to sustain. PwC notes that a portion of companies still use offline or homegrown systems for short-term cash forecasting (22% in the survey highlights shown). Spreadsheets can work, but they raise operational risk (versioning, manual errors) and slow cadence, which is precisely what short-term cash management cannot afford.

5) Automation, AI, and APIs: speeding up the forecasting cycle (with governance)

Treasury technology is increasingly about cycle time: how quickly you can refresh projections after new information arrives. The PwC 2025 Global Treasury Survey reports that 74% are either expanding or actively using AI (machine learning/predictive analysis), and 65% plan to expand API use in the next few years. APIs reduce the “wait time” for bank and ERP data, while automation reduces the human effort required to transform that data into forecast-ready structures.

Benchmarks suggest maturity correlates with automation depth. In the AFP 2025 Treasury Benchmarking Survey, higher-maturity (“strategic/optimized”) treasury teams automate over half of the liquidity-forecast-building process. That automation can include bank data ingestion, reconciliation suggestions, rolling forward recurring items, and variance attribution (e.g., identifying which flows drove the gap between forecast and actual).

However, AI-enabled forecasting needs governance, not blind trust. Research in Jan 2026 (arXiv FinDeepForecast, 2026-01-08) indicates that “agentic” forecasting systems can outperform baselines yet still fall short of true forward-looking reasoning. For treasury, the implication is practical: use AI to improve pattern detection, anomaly flags, and workload reduction, but keep humans accountable for assumptions, scenario design, and sign-offs, especially where forecasts drive funding actions or risk limits.

6) Turning projections into decisions: investing, funding, and working capital

A short-term cash forecast is only as valuable as the actions it enables. One immediate application is short-term cash investing, where the objective is often preservation of principal and liquidity rather than yield. The AFP 2025 Liquidity Survey found that 61% of organizations rank safety as the top short-term investment objective, and bank products are the primary choice for 46% of respondents (responses collected March 4, 28, 2025). A reliable projection clarifies how much cash is truly “excess” and for how long, which is essential for matching tenor to need.

On the funding side, a 13-week view can prevent reactive borrowing. Seeing a liquidity dip forming in week 6, 8 gives time to optimize draw timing, negotiate terms, or accelerate collections. It also supports policy decisions like minimum cash buffers, setting a floor based on forecast volatility rather than an arbitrary number.

Short-term projections also sharpen working-capital programs by quantifying timing effects. If DPO improves (as the Hackett/CFO.com data suggests happened on average in 2024), the forecast should show the resulting cash retention by week and by vendor segment. Likewise, collections initiatives can be tested against near-term liquidity pressure: the question becomes not only “does DSO improve?” but “does cash arrive before the next funding wall?”

7) Cadence and stress: keeping forecasts current and resilient

Forecasts decay quickly when assumptions are static. Rolling forecasts, refreshed monthly or quarterly rather than tied to annual budgets, are emphasized in FP&A practice as a mechanism to reduce reliance on outdated assumptions and improve accuracy (Workday FP&A explainer). Treasury can adopt the same mindset: keep the 13-week model rolling, update drivers routinely, and maintain a consistent weekly (or more frequent) refresh schedule.

For near-term rigor, corporate treasury can borrow concepts from regulated banking disciplines. BCBS intraday liquidity monitoring tools (2013) define granular time-bucketed monitoring and stress scenarios to ensure timely payment and settlement. Corporates aren’t regulated the same way, but the principle still applies: more granular buckets (intraday or daily) improve control over payment timing and reduce the risk of operational surprises.

Finally, stress testing should not be an afterthought. BCBS liquidity risk management principles (2008) emphasize stress tests across institution-specific and market-wide scenarios linked to contingency funding plans. Translating that to a corporate context means running short-horizon scenarios (key customer delay, supplier acceleration, market shock affecting credit availability) and documenting the actions tied to triggers, so the forecast becomes a playbook, not just a report.

Enhancing cash flow management with short-term financial projections is ultimately about reducing uncertainty fast enough to make better decisions. The latest surveys show why this matters now: forecasting is a top priority, it remains challenging, and many teams still carry manual and offline constraints that slow updates and increase error risk. The organizations improving fastest are standardizing horizons, tightening data discipline, and automating repeatable steps.

The goal isn’t a perfect prediction, it’s a reliable operating rhythm. By combining direct-method visibility for the near term, a rolling 13-week runway for control, and modern enablers like APIs and AI (with appropriate governance), treasury teams can align liquidity with real-world actions: invest safely, fund proactively, and convert working-capital performance into cash when it counts.