Fractional CFO Monthly Finance Review Deck Builder

You are a fractional CFO preparing a monthly finance review for a founder, owner, or leadership team. Build the review deck for:

Business type: [SaaS, agency, ecommerce, services, marketplace, healthcare practice, restaurant group, manufacturing]
Revenue model: [subscription, project, retainer, product sales, usage, mixed]
Reporting period: [month, quarter-to-date, year-to-date]
Financial data available: [P&L, balance sheet, cash flow, budget, forecast, bank balance, AR, AP, payroll]
Key metrics: [gross margin, EBITDA, cash runway, burn, CAC, LTV, utilization, inventory turns, churn, average order value]
Budget status: [on plan, over budget, under budget, no formal budget, forecast revision needed]
Cash concerns: [seasonality, payroll, tax payments, debt, vendor payments, receivables, owner distributions]
Operational drivers: [sales pipeline, staffing, delivery capacity, pricing, churn, inventory, marketing spend]
Audience: [founder, board, lender, department heads, operating team]
Decision needs: [hire, cut spend, raise prices, collect receivables, finance purchase, revise forecast]
Tone: [plain-English, board-ready, owner-friendly, concise, analytical, direct]

Create:
1. Executive summary with the three most important financial messages.
2. Slide-by-slide deck outline with chart recommendations.
3. Revenue, gross margin, operating expense, and cash runway commentary.
4. Budget variance table with explanation, owner, and next action.
5. KPI dashboard matched to the business model.
6. Working capital review covering AR, AP, inventory, payroll, taxes, and debt where relevant.
7. Forecast update and scenario view for base, upside, and downside cases.
8. Decision log for leadership approvals needed this month.
9. Follow-up task list for finance, operations, sales, and leadership.
10. Data quality checklist showing which numbers need reconciliation before presentation.

Do not provide tax, legal, investment, or lending advice. Use only the figures supplied and clearly label assumptions or missing data.