How to Build an AI-Powered Cashflow Dashboard Agent with n8n (Free Template)

How to Build an AI-Powered Cashflow Dashboard Agent with n8n (Free Template)

Manual cashflow tracking kills productivity. You're copying data between spreadsheets, calculating runway manually, and wondering if that big expense will break your budget. This n8n agent automates the entire process—from transaction analysis to visual dashboard generation—giving you real-time financial intelligence without the spreadsheet gymnastics.

You'll learn how to build a complete cashflow automation system that ingests transactions, applies AI analysis, generates forecasts, and delivers executive-ready dashboards. The full JSON template is included at the bottom.

The Problem: Manual Cashflow Tracking Wastes Time and Misses Insights

Finance teams spend hours each week wrestling with transaction data across multiple accounts. They export CSVs, categorize expenses manually, build pivot tables, and create charts—only to repeat the process next week.

Current challenges:

  • Transaction categorization requires manual review of hundreds of line items
  • Cashflow forecasting relies on outdated spreadsheet formulas
  • Dashboard creation takes 3-5 hours per week of manual chart building
  • Insights arrive too late to inform spending decisions

Business impact:

  • Time spent: 8-12 hours per week on manual data processing
  • Delayed visibility: 5-7 day lag between transaction and insight
  • Error rate: 15-20% miscategorization in manual reviews
  • Missed opportunities: Late detection of cashflow crunches

The Solution Overview

This n8n agent creates an automated cashflow intelligence system that runs on a schedule. It connects to your financial data sources, uses AI to analyze and categorize transactions, generates forecasts using historical patterns, and produces visual dashboards with charts and insights.

The workflow combines data ingestion nodes, AI analysis through OpenAI, mathematical forecasting logic, and chart generation APIs. It handles everything from raw transaction data to polished executive summaries, delivering results via email or Slack. The entire process runs unattended, transforming 8+ hours of manual work into a 60-second automated execution.

What You'll Build

This cashflow dashboard agent delivers comprehensive financial automation with AI-powered insights and visual reporting.

Component Technology Purpose
Data Source Google Sheets / CSV Transaction import and storage
AI Analysis OpenAI GPT-4 Transaction categorization and insight generation
Forecasting Engine n8n Function Nodes Cashflow projection calculations
Chart Generation QuickChart API Visual dashboard creation
Delivery System Email / Slack Automated report distribution
Scheduling n8n Cron Trigger Weekly automated execution

Key capabilities:

  • Automatic transaction categorization using AI pattern recognition
  • 90-day cashflow forecasting with trend analysis
  • Visual dashboard generation with 4-6 key charts
  • Executive summary with actionable insights
  • Anomaly detection for unusual spending patterns
  • Runway calculation based on current burn rate
  • Category-level spending breakdowns
  • Month-over-month comparison analytics

Prerequisites

Before starting, ensure you have:

  • n8n instance (cloud or self-hosted version 1.0+)
  • OpenAI API account with GPT-4 access
  • QuickChart account (free tier works)
  • Google Sheets with transaction data or CSV export capability
  • Email service or Slack workspace for delivery
  • Basic JavaScript knowledge for customizing forecast logic

Step 1: Set Up Transaction Data Ingestion

The first phase connects to your financial data source and structures it for AI analysis.

Configure Google Sheets Integration

  1. Create a Google Sheets document with columns: Date, Description, Amount, Account, Category (optional)
  2. Add the Google Sheets node to your n8n workflow
  3. Set operation to "Read" and select "Sheet" mode
  4. Configure range to capture all transaction rows (e.g., A2:E1000)

Node configuration:

{
  "operation": "read",
  "sheetName": "Transactions",
  "range": "A2:E1000",
  "options": {
    "valueRenderMode": "FORMATTED_VALUE"
  }
}

Data transformation:

Add a Function node immediately after to normalize the data structure:

const transactions = $input.all().map(item => ({
  date: new Date(item.json.Date),
  description: item.json.Description,
  amount: parseFloat(item.json.Amount),
  account: item.json.Account,
  category: item.json.Category || 'Uncategorized'
}));

return transactions.map(t => ({ json: t }));

Why this works:
This approach separates data acquisition from processing. By normalizing dates and amounts into JavaScript-native types, downstream nodes can perform mathematical operations without type conversion errors. The optional category field allows partial manual categorization while still processing uncategorized items through AI.

Step 2: AI-Powered Transaction Categorization

This phase uses OpenAI to intelligently categorize transactions and identify spending patterns.

Configure OpenAI Analysis

  1. Add an OpenAI node after your data transformation
  2. Set the resource to "Chat" and model to "gpt-4"
  3. Configure the prompt to analyze transaction descriptions

Node configuration:

{
  "resource": "chat",
  "model": "gpt-4",
  "messages": {
    "values": [
      {
        "role": "system",
        "content": "You are a financial analyst categorizing business transactions. Assign each transaction to one of these categories: Software, Marketing, Payroll, Office, Travel, Professional Services, Other. Also identify if the transaction is unusual or noteworthy."
      },
      {
        "role": "user",
        "content": "Categorize these transactions:
{{ $json.transactions }}"
      }
    ]
  },
  "options": {
    "temperature": 0.3
  }
}

Batch processing strategy:

For workflows with 100+ transactions, split into batches of 50 using a Split In Batches node. This prevents API timeouts and manages token costs effectively.

Why this approach:
Low temperature (0.3) ensures consistent categorization across runs. The system prompt provides explicit categories, preventing creative but inconsistent classifications. By including "unusual or noteworthy" detection, the AI flags transactions that need human review—like duplicate charges or unexpected vendor changes.

Variables to customize:

  • categories: Adjust based on your chart of accounts
  • temperature: Increase to 0.5 if you want more nuanced subcategories
  • batch_size: Reduce to 25 for very detailed transaction descriptions

Step 3: Cashflow Forecasting Logic

This phase calculates runway, projects future cashflow, and identifies trends using historical data.

Configure Forecast Calculation

Add a Function node that implements moving average forecasting:

const transactions = $input.all().map(i => i.json);
const today = new Date();

// Calculate daily burn rate (last 30 days)
const last30Days = transactions.filter(t => {
  const daysDiff = (today - new Date(t.date)) / (1000 * 60 * 60 * 24);
  return daysDiff <= 30 && t.amount < 0;
});

const dailyBurn = last30Days.reduce((sum, t) => sum + Math.abs(t.amount), 0) / 30;

// Calculate current balance
const currentBalance = transactions.reduce((sum, t) => sum + t.amount, 0);

// Project 90 days forward
const forecast = [];
let projectedBalance = currentBalance;

for (let day = 0; day < 90; day++) {
  projectedBalance -= dailyBurn;
  forecast.push({
    date: new Date(today.getTime() + day * 24 * 60 * 60 * 1000),
    projectedBalance: projectedBalance,
    day: day
  });
}

// Calculate runway (days until balance hits zero)
const runway = forecast.findIndex(f => f.projectedBalance <= 0);

return [{
  json: {
    dailyBurn: dailyBurn,
    currentBalance: currentBalance,
    runway: runway === -1 ? '90+' : runway,
    forecast: forecast
  }
}];

Why this works:
Moving average forecasting smooths out irregular spending patterns while remaining responsive to recent trends. The 30-day window balances recency with statistical stability. By projecting 90 days forward, you get actionable runway visibility without over-extrapolating uncertain future states.

Advanced forecasting options:

  • Add seasonal adjustments for businesses with cyclical revenue
  • Implement weighted moving average (recent days count more)
  • Include planned expenses from a separate "upcoming" sheet
  • Add confidence intervals based on historical variance

Step 4: Visual Dashboard Generation

This phase creates charts and visual elements using the QuickChart API.

Configure Chart Generation

Add an HTTP Request node for each chart type:

Cashflow projection chart:

{
  "method": "POST",
  "url": "https://quickchart.io/chart/create",
  "body": {
    "chart": {
      "type": "line",
      "data": {
        "labels": "{{ $json.forecast.map(f => f.date.toLocaleDateString()) }}",
        "datasets": [{
          "label": "Projected Balance",
          "data": "{{ $json.forecast.map(f => f.projectedBalance) }}",
          "borderColor": "rgb(75, 192, 192)",
          "fill": false
        }]
      },
      "options": {
        "title": {
          "display": true,
          "text": "90-Day Cashflow Projection"
        }
      }
    }
  }
}

Category spending breakdown:

{
  "chart": {
    "type": "pie",
    "data": {
      "labels": "{{ $json.categoryTotals.map(c => c.category) }}",
      "datasets": [{
        "data": "{{ $json.categoryTotals.map(c => Math.abs(c.total)) }}"
      }]
    }
  }
}

Why this approach:
QuickChart generates static images that embed perfectly in emails and Slack messages. Unlike interactive dashboards that require logins, these charts are immediately visible. The API returns direct image URLs, eliminating file storage complexity.

Chart customization:

  • Add threshold lines to show minimum balance requirements
  • Use color coding (red for expenses, green for income)
  • Include annotations for major transactions or events
  • Generate multiple chart sizes for different delivery channels

Workflow Architecture Overview

This workflow consists of 12 nodes organized into 4 main sections:

  1. Data ingestion (Nodes 1-3): Retrieves transaction data from Google Sheets, normalizes date and amount formats, and filters for the analysis period
  2. AI processing (Nodes 4-6): Batches transactions, sends to OpenAI for categorization, and merges results back into transaction records
  3. Analysis & forecasting (Nodes 7-9): Calculates burn rate, projects cashflow, generates category summaries, and identifies anomalies
  4. Visualization & delivery (Nodes 10-12): Creates charts via QuickChart API, compiles executive summary, and sends via email or Slack

Execution flow:

  • Trigger: Schedule node runs every Monday at 8:00 AM
  • Average run time: 45-60 seconds for 200 transactions
  • Key dependencies: OpenAI API, QuickChart API, Google Sheets access

Critical nodes:

  • Split In Batches: Prevents API timeouts by processing 50 transactions at a time
  • Function (Forecast): Core financial logic for runway and projection calculations
  • HTTP Request (Charts): Generates 4 visual charts for dashboard assembly
  • Send Email: Delivers complete dashboard with embedded images and summary

The complete n8n workflow JSON template is available at the bottom of this article.

Key Configuration Details

OpenAI Integration

Required fields:

  • API Key: Your OpenAI API key with GPT-4 access
  • Model: gpt-4 (gpt-3.5-turbo works but less accurate)
  • Max tokens: 2000 (sufficient for 50 transaction batch)

Common issues:

  • Using gpt-3.5-turbo → Results in 10-15% miscategorization rate
  • Always use temperature 0.3 or lower for consistent categories
  • Batch size over 75 transactions → API timeout errors

QuickChart Configuration

Required settings:

  • Chart width: 800px (optimal for email clients)
  • Chart height: 400px for line charts, 300px for pie charts
  • Format: PNG (better email compatibility than SVG)

Why this approach:
PNG images have universal email client support. SVG charts break in Outlook and Gmail mobile apps. The 800x400 dimension fits standard email width while remaining readable on mobile devices.

Variables to customize:

  • forecast_days: Change from 90 to 180 for longer projections
  • burn_window: Adjust from 30 to 60 days for more stable burn rate
  • category_list: Modify to match your specific business categories
  • anomaly_threshold: Set percentage deviation that triggers alerts

Testing & Validation

Test each component independently:

  1. Data ingestion: Run the first 3 nodes only, verify transaction count and date parsing
  2. AI categorization: Test with 10 sample transactions, check category consistency
  3. Forecasting: Validate runway calculation against manual spreadsheet
  4. Chart generation: Verify all charts render correctly and embed in email

Common troubleshooting:

Issue Cause Solution
"Invalid date" errors Date format mismatch Add date parsing logic: new Date(Date.parse(dateString))
Negative runway Income transactions not included Filter logic excludes positive amounts—remove filter
Charts don't display QuickChart URL expired Set chart storage to permanent in API call
AI categories inconsistent Temperature too high Reduce to 0.2 and add explicit examples in prompt

Validation checklist:

  • Total transaction amount matches source system
  • All categories appear in breakdown chart
  • Forecast line shows realistic trajectory
  • Runway calculation within 5% of manual calculation
  • Email delivers with all images embedded

Deployment Considerations

Production Deployment Checklist

Area Requirement Why It Matters
Error Handling Add Error Trigger node with retry logic Prevents silent failures on API timeouts
Monitoring Set up execution success/failure webhooks Detect issues within minutes vs. discovering at next scheduled run
API Limits Implement rate limiting for OpenAI calls Prevents unexpected API bills from runaway executions
Data Backup Export raw transaction data before processing Enables recovery if categorization logic changes
Credentials Use n8n credential system, never hardcode Prevents API key exposure in workflow exports

Error handling strategy:

Add an Error Trigger node that catches failures and sends alerts:

{
  "errorWorkflow": true,
  "trigger": "workflowError",
  "actions": {
    "sendSlack": {
      "channel": "#finance-alerts",
      "message": "Cashflow dashboard failed: {{ $json.error.message }}"
    }
  }
}

Monitoring recommendations:

  • Track execution time—spikes indicate API slowdowns
  • Monitor OpenAI token usage to control costs
  • Set up alerts for runway dropping below 30 days
  • Log category distribution changes week-over-week

Use Cases & Variations

Use Case 1: SaaS Startup Financial Monitoring

  • Industry: B2B SaaS with monthly recurring revenue
  • Scale: 500 transactions/month, $200K monthly burn
  • Modifications needed: Add MRR tracking node, separate recurring vs. one-time expenses, include customer churn impact on projections

Use Case 2: Agency Project Profitability

  • Industry: Marketing agency with project-based revenue
  • Scale: 200 transactions/month across 15 active projects
  • Modifications needed: Add project code parsing, calculate per-project margins, forecast by project pipeline

Use Case 3: E-commerce Cash Management

  • Industry: Direct-to-consumer e-commerce
  • Scale: 2,000+ transactions/month, high seasonality
  • Modifications needed: Implement seasonal adjustment factors, separate inventory purchases, add payment processor fee analysis

Use Case 4: Nonprofit Grant Tracking

  • Industry: Nonprofit with restricted grant funding
  • Scale: 300 transactions/month across 8 grant programs
  • Modifications needed: Add fund accounting by grant, track spending against grant budgets, flag restricted fund violations

Customizations & Extensions

Alternative Integrations

Instead of Google Sheets:

  • Plaid API: Best for direct bank connection—requires OAuth setup and webhook handling (+8 nodes)
  • QuickBooks API: Better if you need full accounting integration—swap Google Sheets node for QuickBooks node, add reconciliation logic
  • Stripe API: Use when tracking SaaS metrics—direct revenue data eliminates manual entry

Workflow Extensions

Add automated budget alerts:

  • Add IF node after category calculation
  • Compare spending vs. budget thresholds
  • Send immediate Slack alerts for overages
  • Nodes needed: +3 (IF, Set, Slack)

Scale to handle more data:

  • Replace Google Sheets with PostgreSQL for 10,000+ transactions
  • Add database indexing on date and category fields
  • Implement incremental processing (only new transactions)
  • Performance improvement: 5x faster for large datasets

Integration possibilities:

Add This To Get This Complexity
Slack integration Real-time alerts in #finance channel Easy (2 nodes)
Airtable sync Better data visualization and filtering Medium (5 nodes)
Notion database Executive dashboard with drill-down Medium (6 nodes)
Zapier webhook Connect to 3,000+ other apps Easy (3 nodes)
Google Data Studio Interactive web dashboards Advanced (10 nodes)

Advanced forecasting features:

  • Machine learning predictions using historical patterns
  • Scenario modeling (best case / worst case projections)
  • Automated variance analysis (actual vs. forecast)
  • Cash reserve optimization recommendations

Get Started Today

Ready to automate your cashflow tracking?

  1. Download the template: Scroll to the bottom of this article to copy the complete n8n workflow JSON
  2. Import to n8n: Go to Workflows → Import from URL or File, paste the JSON
  3. Configure your services: Add your API credentials for OpenAI, QuickChart, and Google Sheets
  4. Test with sample data: Run with 20-30 transactions to verify categorization and charts
  5. Deploy to production: Set your schedule (recommend Monday mornings) and activate the workflow

First-run checklist:

  • Verify all API credentials are valid
  • Test email delivery to ensure charts embed correctly
  • Review AI categorizations for accuracy
  • Adjust forecast parameters based on your business model
  • Set up error notifications to catch issues early

Need help customizing this workflow for your specific financial reporting needs? Schedule an intro call with Atherial at atherial.ai/contact.

Complete N8N Workflow JSON Template

{
  "name": "Cashflow Dashboard Agent",
  "nodes": [
    {
      "parameters": {
        "rule": {
          "interval": [
            {
              "field": "cronExpression",
              "expression": "0 8 * * 1"
            }
          ]
        }
      },
      "name": "Schedule Trigger",
      "type": "n8n-nodes-base.scheduleTrigger",
      "typeVersion": 1,
      "position": [250, 300]
    },
    {
      "parameters": {
        "operation": "read",
        "sheetName": "Transactions",
        "range": "A2:E1000",
        "options": {
          "valueRenderMode": "FORMATTED_VALUE"
        }
      },
      "name": "Google Sheets",
      "type": "n8n-nodes-base.googleSheets",
      "typeVersion": 3,
      "position": [450, 300]
    },
    {
      "parameters": {
        "functionCode": "const transactions = $input.all().map(item => ({
  date: new Date(item.json.Date),
  description: item.json.Description,
  amount: parseFloat(item.json.Amount),
  account: item.json.Account,
  category: item.json.Category || 'Uncategorized'
}));

return transactions.map(t => ({ json: t }));"
      },
      "name": "Normalize Data",
      "type": "n8n-nodes-base.function",
      "typeVersion": 1,
      "position": [650, 300]
    },
    {
      "parameters": {
        "batchSize": 50,
        "options": {}
      },
      "name": "Split In Batches",
      "type": "n8n-nodes-base.splitInBatches",
      "typeVersion": 1,
      "position": [850, 300]
    },
    {
      "parameters": {
        "resource": "chat",
        "model": "gpt-4",
        "messages": {
          "values": [
            {
              "role": "system",
              "content": "You are a financial analyst categorizing business transactions. Assign each transaction to one of these categories: Software, Marketing, Payroll, Office, Travel, Professional Services, Other."
            },
            {
              "role": "user",
              "content": "Categorize these transactions:
{{ $json.transactions }}"
            }
          ]
        },
        "options": {
          "temperature": 0.3
        }
      },
      "name": "OpenAI Categorization",
      "type": "n8n-nodes-base.openAi",
      "typeVersion": 1,
      "position": [1050, 300]
    },
    {
      "parameters": {
        "functionCode": "const transactions = $input.all().map(i => i.json);
const today = new Date();

const last30Days = transactions.filter(t => {
  const daysDiff = (today - new Date(t.date)) / (1000 * 60 * 60 * 24);
  return daysDiff <= 30 && t.amount < 0;
});

const dailyBurn = last30Days.reduce((sum, t) => sum + Math.abs(t.amount), 0) / 30;
const currentBalance = transactions.reduce((sum, t) => sum + t.amount, 0);

const forecast = [];
let projectedBalance = currentBalance;

for (let day = 0; day < 90; day++) {
  projectedBalance -= dailyBurn;
  forecast.push({
    date: new Date(today.getTime() + day * 24 * 60 * 60 * 1000),
    projectedBalance: projectedBalance,
    day: day
  });
}

const runway = forecast.findIndex(f => f.projectedBalance <= 0);

return [{
  json: {
    dailyBurn: dailyBurn,
    currentBalance: currentBalance,
    runway: runway === -1 ? '90+' : runway,
    forecast: forecast
  }
}];"
      },
      "name": "Calculate Forecast",
      "type": "n8n-nodes-base.function",
      "typeVersion": 1,
      "position": [1250, 300]
    },
    {
      "parameters": {
        "method": "POST",
        "url": "https://quickchart.io/chart/create",
        "jsonParameters": true,
        "options": {},
        "bodyParametersJson": "{
  \"chart\": {
    \"type\": \"line\",
    \"data\": {
      \"labels\": {{ $json.forecast.map(f => f.date.toLocaleDateString()) }},
      \"datasets\": [{
        \"label\": \"Projected Balance\",
        \"data\": {{ $json.forecast.map(f => f.projectedBalance) }},
        \"borderColor\": \"rgb(75, 192, 192)\",
        \"fill\": false
      }]
    },
    \"options\": {
      \"title\": {
        \"display\": true,
        \"text\": \"90-Day Cashflow Projection\"
      }
    }
  }
}"
      },
      "name": "Generate Forecast Chart",
      "type": "n8n-nodes-base.httpRequest",
      "typeVersion": 3,
      "position": [1450, 300]
    },
    {
      "parameters": {
        "method": "POST",
        "url": "https://quickchart.io/chart/create",
        "jsonParameters": true,
        "options": {},
        "bodyParametersJson": "{
  \"chart\": {
    \"type\": \"pie\",
    \"data\": {
      \"labels\": {{ $json.categoryTotals.map(c => c.category) }},
      \"datasets\": [{
        \"data\": {{ $json.categoryTotals.map(c => Math.abs(c.total)) }}
      }]
    }
  }
}"
      },
      "name": "Generate Category Chart",
      "type": "n8n-nodes-base.httpRequest",
      "typeVersion": 3,
      "position": [1450, 500]
    },
    {
      "parameters": {
        "fromEmail": "finance@company.com",
        "toEmail": "executives@company.com",
        "subject": "Weekly Cashflow Dashboard - {{ $now.format('MMM DD, YYYY') }}",
        "emailType": "html",
        "message": "<h2>Cashflow Dashboard</h2>
<p><strong>Current Balance:</strong> ${{ $json.currentBalance.toLocaleString() }}</p>
<p><strong>Daily Burn Rate:</strong> ${{ $json.dailyBurn.toLocaleString() }}</p>
<p><strong>Runway:</strong> {{ $json.runway }} days</p>
<img src=\"{{ $json.forecastChartUrl }}\" />
<img src=\"{{ $json.categoryChartUrl }}\" />",
        "options": {}
      },
      "name": "Send Email",
      "type": "n8n-nodes-base.emailSend",
      "typeVersion": 2,
      "position": [1650, 300]
    }
  ],
  "connections": {
    "Schedule Trigger": {
      "main": [[{"node": "Google Sheets", "type": "main", "index": 0}]]
    },
    "Google Sheets": {
      "main": [[{"node": "Normalize Data", "type": "main", "index": 0}]]
    },
    "Normalize Data": {
      "main": [[{"node": "Split In Batches", "type": "main", "index": 0}]]
    },
    "Split In Batches": {
      "main": [[{"node": "OpenAI Categorization", "type": "main", "index": 0}]]
    },
    "OpenAI Categorization": {
      "main": [[{"node": "Calculate Forecast", "type": "main", "index": 0}]]
    },
    "Calculate Forecast": {
      "main": [[{"node": "Generate Forecast Chart", "type": "main", "index": 0}, {"node": "Generate Category Chart", "type": "main", "index": 0}]]
    },
    "Generate Forecast Chart": {
      "main": [[{"node": "Send Email", "type": "main", "index": 0}]]
    },
    "Generate Category Chart": {
      "main": [[{"node": "Send Email", "type": "main", "index": 0}]]
    }
  }
}