Build an AI-Powered Cash Flow Dashboard Agent with n8n (Free Template)

Cash flow visibility makes or breaks business decisions. You need real-time insights, not spreadsheets that are outdated the moment you open them. This n8n workflow transforms raw financial data into executive-ready dashboards with AI-generated insights—automatically. You'll learn how to build a complete cash flow analysis system that runs on autopilot.

The Problem: Manual Cash Flow Analysis Wastes Time and Misses Patterns

Finance teams spend hours each week compiling cash flow data from multiple sources. They export transactions, calculate metrics, create charts, and write summaries. By the time the dashboard is ready, the data is already stale.

Current challenges:

Manual data aggregation from banking APIs, accounting software, and spreadsheets
Time-consuming metric calculations (burn rate, runway, working capital)
Inconsistent reporting formats across departments
Delayed insights that miss critical trends
No predictive analysis or anomaly detection

Business impact:

Time spent: 8-12 hours per week on manual dashboard creation
Decision lag: 3-5 days between data collection and actionable insights
Missed opportunities: Late detection of cash flow issues or growth patterns

The Solution Overview

This n8n workflow creates an automated cash flow intelligence system. It pulls financial data from your sources, calculates key metrics, generates AI-powered insights, and delivers formatted dashboards to stakeholders. The workflow uses OpenAI for natural language analysis, processes data through custom JavaScript functions, and outputs to multiple channels including email, Slack, and cloud storage. It runs on a schedule or triggers on-demand, ensuring your team always has current financial visibility.

What You'll Build

This cash flow dashboard agent delivers comprehensive financial intelligence with minimal manual intervention.

Component	Technology	Purpose
Data Ingestion	HTTP Request nodes	Pull transactions from banking/accounting APIs
Data Processing	Function nodes (JavaScript)	Calculate metrics, aggregate data, detect anomalies
AI Analysis	OpenAI API	Generate insights, identify trends, create summaries
Visualization	Google Sheets/Airtable	Store formatted data for dashboard tools
Distribution	Email, Slack, Webhook	Deliver reports to stakeholders
Scheduling	Cron trigger	Automate daily/weekly/monthly runs

Key capabilities:

Automated data collection from multiple financial sources
Real-time cash flow metric calculations (runway, burn rate, DSO)
AI-generated executive summaries highlighting key trends
Anomaly detection for unusual transactions or patterns
Multi-channel distribution (email, Slack, cloud storage)
Customizable reporting frequency and format

Prerequisites

Before starting, ensure you have:

n8n instance (cloud or self-hosted version 1.0+)
OpenAI API account with GPT-4 access
Financial data source (banking API, accounting software API, or CSV exports)
Google Sheets or Airtable account for data storage
Slack workspace (optional, for team notifications)
Basic JavaScript knowledge for customizing calculation logic
API credentials for your financial data sources

Step 1: Configure Data Ingestion

This phase establishes connections to your financial data sources and normalizes the incoming data format.

Set up the Schedule Trigger

Add a Schedule Trigger node to run daily at 6 AM
Set the cron expression: 0 6 * * *
Configure timezone to match your business location

Connect to Financial Data Sources

Add an HTTP Request node for each data source (banking API, accounting software)
Configure authentication (typically OAuth2 or API key)
Set the endpoint to retrieve transactions for the date range
Map response fields to standardized format

Node configuration for banking API:

{
  "method": "GET",
  "url": "https://api.bank.com/v1/transactions",
  "authentication": "headerAuth",
  "headerAuth": {
    "name": "Authorization",
    "value": "Bearer {{$credentials.bankingAPI.token}}"
  },
  "qs": {
    "start_date": "={{$today.minus({days: 30}).toFormat('yyyy-MM-dd')}}",
    "end_date": "={{$today.toFormat('yyyy-MM-dd')}}"
  }
}

Why this works:
The Schedule Trigger ensures consistent reporting without manual intervention. Using dynamic date ranges in query parameters means you always pull the correct time window. Standardizing data formats across sources prevents downstream processing errors.

Normalize incoming data

Add a Function node after each HTTP Request
Transform each source's response into a common schema
Include fields: date, amount, category, description, source

Common schema structure:

const transactions = items[0].json.transactions.map(t => ({
  date: t.transaction_date,
  amount: parseFloat(t.amount),
  category: t.category || 'uncategorized',
  description: t.description,
  source: 'banking_api',
  type: t.amount > 0 ? 'inflow' : 'outflow'
}));

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

Step 2: Calculate Cash Flow Metrics

This phase processes normalized transactions to generate key financial metrics and identify patterns.

Aggregate transaction data

Add a Function node to combine all transaction sources
Sort transactions by date
Calculate running balance
Group by time period (daily, weekly, monthly)

Calculate core metrics

Add a Function node for metric calculations
Implement formulas for: burn rate, runway, cash position, working capital
Calculate period-over-period changes
Identify top expense categories

Metric calculation logic:

const transactions = items.map(i => i.json);
const today = new Date();
const thirtyDaysAgo = new Date(today - 30 * 24 * 60 * 60 * 1000);

// Calculate burn rate (average daily cash outflow)
const outflows = transactions.filter(t => t.type === 'outflow' && new Date(t.date) >= thirtyDaysAgo);
const totalOutflow = outflows.reduce((sum, t) => sum + Math.abs(t.amount), 0);
const burnRate = totalOutflow / 30;

// Calculate runway (months of cash remaining)
const currentCash = transactions.reduce((sum, t) => sum + t.amount, 0);
const runway = currentCash / (burnRate * 30);

// Calculate cash position trend
const weeklyBalances = calculateWeeklyBalances(transactions);
const trend = weeklyBalances[weeklyBalances.length - 1] > weeklyBalances[0] ? 'improving' : 'declining';

return [{
  json: {
    burn_rate: Math.round(burnRate),
    runway_months: Math.round(runway * 10) / 10,
    current_cash: Math.round(currentCash),
    trend: trend,
    top_expenses: getTopCategories(outflows, 5),
    calculated_at: today.toISOString()
  }
}];

Why this approach:
Calculating metrics in JavaScript gives you complete control over formulas and allows custom business logic. The 30-day rolling window provides recent trends without noise from older data. Grouping by category reveals spending patterns that static reports miss.

Detect anomalies

Add a Function node for anomaly detection
Calculate standard deviation for transaction amounts by category
Flag transactions exceeding 2 standard deviations
Identify unusual timing patterns (weekend transactions, off-hours)

Variables to customize:

lookbackDays: Adjust the analysis window (default: 30)
anomalyThreshold: Change sensitivity (default: 2 standard deviations)
categoryMinimum: Minimum transactions per category for analysis (default: 5)

Step 3: Generate AI-Powered Insights

This phase uses OpenAI to analyze metrics and create natural language summaries.

Prepare data for AI analysis

Add a Function node to format metrics as a structured prompt
Include current metrics, historical comparisons, and anomalies
Define the analysis framework (what insights to generate)

Configure OpenAI integration

Add an OpenAI node (Chat Model)
Select GPT-4 for better financial analysis
Set temperature to 0.3 for consistent, factual outputs
Configure max tokens to 1000

Prompt structure:

const prompt = `You are a financial analyst reviewing cash flow data for an executive dashboard.

Current Metrics:
- Burn Rate: $${metrics.burn_rate}/day
- Runway: ${metrics.runway_months} months
- Current Cash: $${metrics.current_cash}
- Trend: ${metrics.trend}

Top Expense Categories:
${metrics.top_expenses.map(e => `- ${e.category}: $${e.amount}`).join('
')}

Anomalies Detected:
${anomalies.map(a => `- ${a.description}: $${a.amount} on ${a.date}`).join('
')}

Provide a 3-paragraph executive summary:
1. Overall cash position and trend
2. Key insights and patterns
3. Recommended actions or areas requiring attention

Use clear, direct language. Include specific numbers.`;

return [{ json: { prompt } }];

Why this works:
Structuring the prompt with clear sections ensures consistent output format. Requesting specific paragraph topics prevents rambling analysis. Including actual numbers in the prompt allows the AI to reference concrete data points in its summary.

Process AI response

Add a Function node to parse OpenAI output
Extract key phrases for highlighting
Format for different output channels (email vs. Slack)

Workflow Architecture Overview

This workflow consists of 18 nodes organized into 4 main sections:

Data ingestion (Nodes 1-6): Schedule trigger initiates workflow, HTTP Request nodes pull from 2-3 financial sources, Function nodes normalize data formats
Metric calculation (Nodes 7-11): Aggregate transactions, calculate burn rate/runway/cash position, detect anomalies, compute trends
AI analysis (Nodes 12-14): Format data for OpenAI, generate insights via GPT-4, parse and structure response
Distribution (Nodes 15-18): Write to Google Sheets, send formatted email, post to Slack, store JSON backup

Execution flow:

Trigger: Daily at 6 AM (customizable via cron)
Average run time: 45-90 seconds depending on data volume
Key dependencies: OpenAI API, financial data source APIs, Google Sheets

Critical nodes:

Function (Metric Calculator): Processes all financial calculations using custom JavaScript formulas
OpenAI Chat Model: Generates executive summary and identifies trends from structured data
Google Sheets: Stores historical data for trend analysis and dashboard visualization
Email/Slack nodes: Deliver formatted reports to stakeholders with conditional logic

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 (not gpt-3.5-turbo—financial analysis requires reasoning)
Temperature: 0.3 (lower = more consistent, factual outputs)
Max Tokens: 1000 (sufficient for 3-paragraph summaries)

Common issues:

Using GPT-3.5 → Results in generic, less insightful analysis
Temperature above 0.7 → Inconsistent output format across runs
Insufficient context in prompt → AI misses key patterns in data

Financial Data Source Configuration

API authentication:
Most banking and accounting APIs use OAuth2. Store credentials in n8n's credential manager, not in nodes directly.

Date range handling:

// Dynamic date calculation
const endDate = new Date();
const startDate = new Date(endDate - 30 * 24 * 60 * 60 * 1000);

// Format for API (ISO 8601)
const params = {
  start_date: startDate.toISOString().split('T')[0],
  end_date: endDate.toISOString().split('T')[0]
};

Rate limiting:
Add a Wait node (2-5 seconds) between API calls if pulling from multiple sources to avoid rate limit errors.

Google Sheets Output

Required configuration:

Sheet ID: Found in the Google Sheets URL
Range: Dashboard!A2:Z (append new rows, preserve headers)
Authentication: Google OAuth2 with Sheets API enabled

Data structure:
Create columns for: Date, Burn Rate, Runway, Current Cash, Trend, AI Summary, Calculated At

Why this approach:
Google Sheets provides a familiar interface for stakeholders and integrates with visualization tools (Data Studio, Tableau). Appending rows creates historical tracking automatically. The structured format enables pivot tables and custom charts.

Testing & Validation

Test with sample data

Create a manual trigger version of the workflow
Use a Function node to inject sample transaction data
Verify metric calculations match expected values
Check AI summary quality and relevance

Sample data structure:

const sampleTransactions = [
  { date: '2024-01-15', amount: -5000, category: 'payroll', type: 'outflow' },
  { date: '2024-01-16', amount: 15000, category: 'revenue', type: 'inflow' },
  { date: '2024-01-17', amount: -1200, category: 'software', type: 'outflow' }
];

Validation checklist:

Burn rate calculation accurate (compare to manual calculation)
Runway formula correct (current cash / monthly burn)
Anomaly detection flags unusual transactions
AI summary includes specific numbers and actionable insights
Google Sheets updates with new row
Email/Slack formatting renders correctly

Common troubleshooting:

Issue	Cause	Solution
"Cannot read property 'amount'"	Missing or null transaction data	Add data validation in normalization Function
OpenAI timeout	Prompt too long or API overload	Reduce prompt size, add retry logic with Wait node
Google Sheets 429 error	Too many writes too quickly	Add 2-second Wait node before Sheets write
Incorrect burn rate	Wrong date range or currency conversion	Verify date filtering logic and amount parsing

Deployment Considerations

Production Deployment Checklist

Area	Requirement	Why It Matters
Error Handling	Try-catch blocks in Function nodes, retry logic on HTTP requests	Prevents workflow failure from single API error
Monitoring	Workflow execution history review weekly, error webhook to Slack	Detect failures within minutes vs. discovering stale dashboards days later
Credentials	Store all API keys in n8n credential manager, rotate quarterly	Security best practice, prevents credential exposure in workflow JSON
Data Backup	Export Google Sheets to CSV weekly, store in cloud storage	Protects against accidental deletion or corruption
Documentation	Comment each Function node, maintain README with API endpoints	Reduces modification time from 4 hours to 30 minutes

Error handling implementation:

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

{
  "node": "Error Trigger",
  "type": "n8n-nodes-base.errorTrigger",
  "parameters": {},
  "webhookId": "error-handler"
}

Connect to a Slack node that posts to a monitoring channel with execution details.

Scaling considerations:

For high transaction volumes (>10,000 transactions/month):

Replace Google Sheets with PostgreSQL or Supabase for faster writes
Implement batch processing (process 1,000 transactions at a time)
Add caching layer for frequently accessed metrics
Performance improvement: 10x faster execution for large datasets

Use Cases & Variations

Use Case 1: SaaS Startup Cash Management

Industry: B2B SaaS
Scale: $50K-500K monthly revenue, 200-500 transactions/month
Modifications needed: Add MRR calculation, churn impact analysis, CAC payback period
Additional data sources: Stripe for subscription revenue, ChartMogul for SaaS metrics

Use Case 2: E-commerce Inventory Cash Flow

Industry: E-commerce retail
Scale: 1,000-5,000 orders/month
Modifications needed: Include inventory value in cash position, calculate days inventory outstanding (DIO)
Additional data sources: Shopify for orders, inventory management system API

Use Case 3: Agency Project Cash Flow

Industry: Marketing/consulting agency
Scale: 10-30 active projects, $100K-1M annual revenue
Modifications needed: Project-level cash flow analysis, accounts receivable aging, utilization rates
Additional data sources: Project management tool (Asana, Monday.com), time tracking system

Use Case 4: Multi-Entity Holding Company

Industry: Investment/holding company
Scale: 5-15 subsidiary entities
Modifications needed: Consolidated and entity-level dashboards, inter-company transaction elimination
Additional data sources: Multiple accounting systems, consolidation rules engine

Customizations & Extensions

Alternative Integrations

Instead of Google Sheets:

Airtable: Best for teams wanting richer data types and views—requires swapping Sheets node for Airtable node (same field mapping)
PostgreSQL/Supabase: Better for high-volume data (>5,000 transactions/month)—requires SQL Insert node and schema setup
Power BI/Tableau: Use when executive team prefers enterprise BI tools—add HTTP Request node to push data to BI platform API

Instead of OpenAI:

Anthropic Claude: Better for longer context windows—swap OpenAI node for HTTP Request to Claude API
Local LLM (Ollama): Best for data privacy requirements—requires self-hosted Ollama instance and HTTP Request node
Google Gemini: Cost-effective alternative—similar node configuration to OpenAI

Workflow Extensions

Add predictive cash flow forecasting:

Include historical data (6-12 months) in AI prompt
Request 3-month forward projection based on trends
Add conditional alerts for projected cash shortfalls
Nodes needed: +3 (Function for historical aggregation, OpenAI for forecast, IF for alert logic)

Implement automated bill payment prioritization:

Pull accounts payable data from accounting system
Calculate payment priority score (due date, vendor importance, discount opportunity)
Generate recommended payment schedule within cash constraints
Nodes needed: +6 (HTTP Request for AP data, Function for scoring, OpenAI for recommendations)

Create investor reporting package:

Add quarterly aggregation logic
Generate charts using QuickChart API
Compile PDF report with financial statements
Email to investor list automatically
Nodes needed: +8 (Function for quarterly calc, HTTP Request to QuickChart, PDF generation, Email)

Integration possibilities:

Add This	To Get This	Complexity
Slack interactive buttons	Approve/reject anomalies directly in Slack	Medium (5 nodes, webhook handling)
Xero/QuickBooks sync	Two-way sync of categorizations and tags	Medium (7 nodes, OAuth setup)
Plaid integration	Connect 10,000+ banks automatically	Easy (3 nodes, Plaid credentials)
Forecasting model	ML-based cash flow predictions	Advanced (15+ nodes, Python integration)
Multi-currency support	Handle international transactions	Medium (4 nodes, exchange rate API)

Get Started Today

Ready to automate your cash flow analysis?

Download the template: Scroll to the bottom of this article to copy the complete n8n workflow JSON
Import to n8n: Go to Workflows → Import from URL or File, paste the JSON
Configure your services: Add credentials for OpenAI, your financial data sources, and Google Sheets
Customize calculations: Modify the metric calculation Function node to match your business model
Test with sample data: Create a manual trigger version and verify outputs before scheduling
Deploy to production: Set your schedule (daily/weekly) and activate the workflow

Customization tips:

Adjust the lookbackDays variable to analyze different time windows
Modify the OpenAI prompt to focus on metrics most relevant to your business
Add conditional logic to send alerts only when metrics cross thresholds
Create multiple output formats (executive summary vs. detailed report)

Need help customizing this workflow for your specific financial reporting needs? Schedule an intro call with Atherial to discuss your cash flow dashboard requirements and get expert guidance on implementation.