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Cash Forecasting

Overview

Cash forecasting is the process of predicting future cash inflows and outflows to ensure a company has adequate liquidity to meet its obligations. For treasury teams, this ranges from short-term daily/weekly forecasts (operational) to longer-term monthly/quarterly projections (strategic planning).

Accurate cash forecasting enables treasury teams to optimize working capital, make informed investment decisions, manage debt facilities, and avoid costly surprises. Poor forecasting leads to either excess idle cash (opportunity cost) or dangerous liquidity shortfalls.

Key challenges include decentralized data sources, infrequent forecast updates, difficulty reconciling forecast vs actual, and limited visibility into working capital drivers. Palm can help by aggregating data across entities, enabling entity-level forecasting with group consolidation, and providing variance analysis tools.

For detailed ICP context and terminology, see fundamentals.md

Top Jobs & Desired Outcomes

Full history: jobs.md

⚡ 1. Explain and demonstrate forecast reliability to stakeholders

Desired Outcomes: - Minimize the effort required to demonstrate forecast accuracy - Increase the transparency of model selection and performance - Reduce skepticism from finance stakeholders about ML-based forecasts - Increase ability to answer "can I trust it?" with data - Increase ability to investigate root causes of forecast misses

Sources: ON x5 (Confirmed) - "Can I trust it? Machine learning can be such a big thing." / "Palm is definitively the superior model since it provides a stable, single-digit risk environment on the short term."

⚡ 2. Produce a short-term cash forecast I can trust and act on

Desired Outcomes: - Minimize the time spent updating daily forecast (target: 15 minutes or less) - Increase the percentage of forecast based on booked/actual data vs guesses - Reduce the frequency of surprise payments not captured in forecast - Minimize the variance between forecasted and actual balances at quarter end

Sources: ON x4, Sonder, Levi's (Confirmed) - T+7 benchmark: ~90% reliability; "Palm establishes a highly reliable baseline at just 4.5%"; "My treasurer just wants a button... push a button."

⚡ 3. Detect missing or anomalous items in entity forecasts proactively

Desired Outcomes: - Minimize the risk of missing cyclical payments (payroll, tax) in entity forecasts - Reduce late-day cash surprises caused by unforeseen large transactions - Increase proactive detection of anomalous forecast inputs before they impact positioning

Sources: Volvo Cars, IHG, ON (Confirmed) - "Sometimes the guys in India will just miss... even a payroll run has been missed off"; "$8M IC payment from Canada failed and no one noticed"

⚡ 4. Understand and challenge forecast model assumptions at category level

Desired Outcomes: - Minimize the time required to understand what assumptions the forecast model has applied to a category - Increase the ability to override model assumptions when external context contradicts the trend - Reduce the risk of double-counting a trend already captured by the model when adding manual adjustments

Sources: Personio, Instacart (Confirmed) - "I want to see what rules generated each forecast number... variance should be split into actuals variance vs assumption change variance"

⚡ 5. Optimize use of operational cash

Desired Outcomes: - Minimize buffer balances held on operational accounts - Increase investment income by deploying excess cash to higher-yield investments - Reduce uncertainty about daily funding needs

Sources: Personio x2, Sonder (Confirmed) - "Cash forecasting from a Treasury perspective is purely around efficiency... having full visibility of flows in and out at a transactional level allows you to be fully efficient with your cash."

Key Pain Points

Full history: pain-points.md

  • Can't easily explain forecast accuracy - Hard to answer "can I trust it?" from stakeholders (Source: ON)
  • No visibility into model selection - Don't know which models are being used or why (Source: ON)
  • Excel-based validation workflows unsustainable - Manual WMAPE tracking hard to maintain (Source: ON)
  • No built-in forecast version comparison - Have to manually compare different forecast versions (Source: ON)
  • 150 accounts blindness - "You become fully blind. You don't know where to draw your attention." (Source: ON)
  • Holding buffers because forecast isn't reliable enough - Can't do just-in-time funding (Sources: Personio x2)
  • Manual spreadsheet process - No system, everything basic and manual (Source: Personio)
  • Last-minute surprise payments - Payments not in anyone's radar, discovered day-of (Source: ON)
  • No smart forecasting in TMS - Kyriba just does open items, no ML/patterns (Source: ON)
  • Outliers skew forecasts - Large one-off payments distort future predictions (Sources: ON, Sonder)
  • Decentralized entities make consolidated forecasting difficult - Multiple entities need individual and group views (Sources: Euroports, Volvo Cars)
  • Data fragmentation industry-wide - "The biggest pain is in gathering all the data into one platform" (Source: Treasury Dragons - Cobase)
  • Spreadsheet default despite tools - 78% of treasurers want better forecasts; many revert to Excel (Source: Treasury Dragons - Ferguson)

Key Opportunities

  • Forecast explainability/trust - Answer "can I trust it?" with model performance visibility
  • Outlier handling - Mark transactions as one-offs to exclude from learning
  • Entity-by-entity forecasting with group consolidation - Enable local input with central visibility
  • Category-level views - See development of salaries, Capex, etc. separately
  • AR pattern integration - Apply customer payment behaviors to collection forecasts
  • Pattern detection - Proactively detect missing cyclical items (e.g., UK tax every March)
  • Discrepancy reporting - Report variances vs overwriting forecasts to improve accountability
  • Top customer tracking - Focus ML forecasting on influential top 10 customers rather than all
  • Bank statement ML foundation - Use bank statements as primary ML data source
  • Push button forecasting - One-click weekly forecast generation
  • Confidence + accuracy - Users need to trust HOW forecast was generated, not just see the number
  • Blueprint for liquidity management - Forecast as dynamic decision tool, not static report

Open Questions

  • [ ] What's the right balance between local entity input and central forecasting?
  • [ ] How to handle factored receivables in cash forecasting?
  • [ ] How to surface model performance without overwhelming non-technical users?
  • [ ] How to implement "calendar per input" for different seasonality patterns?

Last updated: 2026-03-10 | Sources: 29 transcripts (view all)