Cash forecasting starts with operational data, not an algorithm label. A useful baseline combines transaction history, denomination mix, calendar effects, replenishment events, outages, and location context. Missing timestamps, inconsistent device identifiers, and unrecorded manual overrides can undermine a sophisticated model before it reaches production.
The first comparison should be against the organisation's existing rule, not an external benchmark. Measure forecast error by location and horizon, then track the operational consequences: emergency orders, stockouts, excess holdings, route changes, and manual overrides. Different cashpoints may require different models or confidence thresholds.
Governance matters as much as accuracy. Teams need versioned data, documented features, approval rules, confidence bands, and a clear fallback when the model is uncertain. Forecasts should support a human decision and produce an audit trail rather than silently changing operational orders.
CashPrime treats forecasting outcomes as a planning target to validate during a pilot. A credible business case records the current baseline, defines the decision the forecast will improve, and measures whether operational exceptions decline without harming cash availability.