Forecasting Cash Flow from Paystack Data

// Prepare aggregated data for LLM — never raw transactions
async function buildForecastInput() {
  var headers = { Authorization: 'Bearer ' + process.env.PAYSTACK_SECRET_KEY };

  // 1. Monthly revenue for past 6 months
  var monthlyRevenue = [];
  for (var i = 5; i >= 0; i--) {
    var d = new Date();
    d.setMonth(d.getMonth() - i);
    var from = new Date(d.getFullYear(), d.getMonth(), 1).toISOString().slice(0, 10);
    var to = new Date(d.getFullYear(), d.getMonth() + 1, 0).toISOString().slice(0, 10);
    var res = await fetch('https://api.paystack.co/transaction?status=success&from=' + from + '&to=' + to + '&perPage=100', { headers });
    var txns = (await res.json()).data || [];
    monthlyRevenue.push({
      month: from.slice(0, 7),
      total_ngn: txns.reduce((s, t) => s + t.amount, 0) / 100,
      count: txns.length,
    });
  }

  // 2. Current MRR from active subscriptions
  var subsRes = await fetch('https://api.paystack.co/subscription?status=active&perPage=100', { headers });
  var subs = (await subsRes.json()).data || [];
  var mrr = subs.reduce((s, sub) => s + sub.amount, 0) / 100;

  // 3. Settlement lag (from recent settlements)
  var settleRes = await fetch('https://api.paystack.co/settlement?perPage=10', { headers });
  var settlements = (await settleRes.json()).data || [];
  var avgLagDays = settlements.length
    ? settlements.reduce((s, st) => {
        var txDate = new Date(st.effective_date);
        var settleDate = new Date(st.settlement_date);
        return s + (settleDate - txDate) / 86400000;
      }, 0) / settlements.length
    : 1; // default T+1

  return { monthlyRevenue, mrr_ngn: mrr, active_subscriptions: subs.length, avg_settlement_lag_days: avgLagDays };
}

// Feed to LLM for forecast
async function generateCashFlowForecast() {
  var data = await buildForecastInput();

  var prompt = `Here is our Paystack revenue data for the past 6 months:
Monthly revenue: ${JSON.stringify(data.monthlyRevenue)}
Current MRR from active subscriptions: NGN ${data.mrr_ngn}
Active subscriptions: ${data.active_subscriptions}
Average settlement lag: ${data.avg_settlement_lag_days.toFixed(1)} days

Based on this data, forecast next month's revenue (low/mid/high range).
State the assumptions driving each scenario.
Highlight any trends you see in the monthly data.
Note: Paystack settles net of fees (~1.5% + NGN 100 per transaction).`;

  var response = await anthropic.messages.create({
    model: 'claude-opus-4-6',
    max_tokens: 1024,
    messages: [{ role: 'user', content: prompt }],
  });

  return response.content[0].text;
}

See building a natural language revenue dashboard for the interactive query pattern over the same data.

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