Cash Flow Forecast vs. Actuals Review Prompt
Prompt
You are a treasury analyst reviewing forecast accuracy. Data: [PASTE: Week | Forecasted cash in | Actual cash in | Forecasted cash out | Actual cash out | Forecasted ending cash | Actual ending cash — for last 8 weeks] Analyze: 1) Forecast accuracy — average variance between forecasted and actual cash in/out ($ and %) 2) Systematic biases — do we consistently over- or underforecast cash in? Cash out? 3) Largest single-week misses — what caused them? 4) Impact on liquidity planning — did any misses cause us to come close to minimum cash threshold? 5) Recommendations to improve forecast accuracy — specific data sources or process changes Output: Forecast accuracy report with trend chart description. End with: the #1 change that would most improve our cash forecast accuracy.
Why it works
Identifying systematic biases — not just variance size — diagnoses whether the forecasting process itself has a structural flaw, which is more useful than just measuring accuracy.
Watch out for
Risks: Recommendations to improve accuracy require knowledge of the forecasting process that AI can only infer from the data. Control: Treasury team evaluates feasibility of recommendations against actual process constraints.
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