Personal finance apps collect data. AI-powered finance coaches transform that data into actionable guidance. The difference? Intelligent automation that turns raw financial information into personalized insights, recommendations, and proactive alerts. This article teaches you how to build the automation backbone of an AI finance coach using n8n—the workflow engine that connects your data sources, AI models, and user touchpoints into a cohesive system.
You'll learn how to architect multi-stage workflows that ingest financial data, process it through AI analysis, and deliver personalized coaching through automated channels. By the end, you'll have a complete n8n template ready to customize for your specific use case.
The Problem: Manual Financial Guidance Doesn't Scale
Financial coaching requires continuous monitoring, pattern recognition, and timely intervention. Human advisors can't watch every transaction, analyze spending patterns in real-time, or deliver instant guidance when users need it most.
Current challenges:
- Financial advisors spend 60-70% of their time on data gathering and basic analysis instead of high-value coaching
- Users receive generic advice that doesn't account for their specific behavioral patterns or financial context
- Critical financial moments (overspending alerts, savings opportunities) go unnoticed until monthly reviews
- Scaling personalized guidance requires hiring proportionally more advisors
Business impact:
- Time spent: 15-20 hours per client per month on manual data analysis
- Delayed interventions: 7-14 day lag between financial events and advisor response
- Limited scalability: Each advisor can effectively manage only 50-75 clients with personalized attention
Manual processes create a ceiling on both service quality and business growth. Automation removes that ceiling.
The Solution Overview
An AI-powered finance coach built with n8n creates an always-on system that monitors financial data, analyzes patterns, generates insights, and delivers personalized guidance automatically. The workflow connects data sources (bank APIs, transaction feeds, user inputs), processes information through AI models for analysis and recommendation generation, and distributes coaching content through multiple channels (email, in-app notifications, SMS).
This approach combines three critical components: real-time data ingestion pipelines, AI-driven analysis engines, and multi-channel delivery systems. n8n orchestrates these components, handling the complexity of API integrations, data transformations, conditional logic, and scheduled executions. The result is a system that scales personalized financial coaching without proportional increases in human labor.
What You'll Build
This n8n automation system delivers comprehensive financial coaching capabilities through intelligent workflow orchestration.
| Component | Technology | Purpose |
|---|---|---|
| Data Ingestion | Webhook/HTTP Request nodes | Pull transaction data from Plaid, Stripe, or banking APIs |
| Data Storage | Redis/PostgreSQL integration | Cache frequently accessed data and store user financial profiles |
| AI Analysis | OpenAI/Claude API nodes | Generate spending insights, budget recommendations, and financial forecasts |
| Business Logic | Function/Code nodes | Calculate financial metrics, detect anomalies, apply coaching rules |
| User Segmentation | Switch/IF nodes | Route users to appropriate coaching workflows based on financial behavior |
| Content Generation | AI + Template nodes | Create personalized coaching messages, reports, and action plans |
| Multi-Channel Delivery | Email/Slack/SMS nodes | Send guidance through user's preferred communication channels |
| Scheduling Engine | Cron/Schedule nodes | Trigger daily analysis, weekly reports, monthly reviews automatically |
| Error Handling | Error Workflow nodes | Capture failures, retry logic, alert monitoring systems |
Prerequisites
Before starting, ensure you have:
- n8n instance (cloud or self-hosted with minimum 2GB RAM for AI processing)
- Financial data source API access (Plaid, Stripe, or banking API with OAuth configured)
- AI service account (OpenAI API key with GPT-4 access or Anthropic Claude)
- Redis instance for caching (optional but recommended for production)
- Email service credentials (SendGrid, Mailgun, or SMTP)
- Basic JavaScript knowledge for Function nodes and data transformations
- Understanding of financial data structures (transactions, accounts, balances)
Step 1: Set Up Financial Data Ingestion Pipeline
Your finance coach needs continuous access to user financial data. This phase establishes the data pipeline that feeds your AI analysis engine.
Configure Webhook Trigger
- Add a Webhook node set to POST method at
/webhook/financial-data - Enable "Respond Immediately" to acknowledge receipt without blocking
- Set authentication to header-based with custom token validation
Connect to Financial Data Source
- Add HTTP Request node configured for your data provider
- For Plaid integration: Use
/transactions/getendpoint with date range parameters - Set authentication to OAuth2 with refresh token handling
- Configure retry logic: 3 attempts with exponential backoff (2s, 4s, 8s)
Node configuration:
{
"method": "POST",
"url": "https://production.plaid.com/transactions/get",
"authentication": "oAuth2",
"sendBody": true,
"bodyParameters": {
"client_id": "={{$env.PLAID_CLIENT_ID}}",
"secret": "={{$env.PLAID_SECRET}}",
"access_token": "={{$json.user_access_token}}",
"start_date": "={{$now.minus({days: 30}).toFormat('yyyy-MM-dd')}}",
"end_date": "={{$now.toFormat('yyyy-MM-dd')}}"
}
}
Why this works:
This configuration pulls 30 days of transaction history on each execution, providing sufficient context for AI analysis without overwhelming the system. The exponential backoff retry logic handles temporary API failures gracefully, preventing data loss during network hiccups or rate limiting.
Variables to customize:
start_dateandend_date: Adjust the lookback window based on your coaching model (7 days for real-time alerts, 90 days for trend analysis)retry_attempts: Increase to 5 for critical financial data that must not be lost
Step 2: Transform and Enrich Financial Data
Raw transaction data needs standardization and enrichment before AI analysis produces meaningful insights.
Normalize Transaction Data
- Add Function node to standardize transaction formats across different data sources
- Extract key fields: amount, date, merchant, category, account_id
- Calculate derived metrics: daily spending, category totals, unusual transaction flags
Code for normalization:
const transactions = $input.all()[0].json.transactions;
return transactions.map(txn => ({
id: txn.transaction_id,
amount: Math.abs(txn.amount),
date: txn.date,
merchant: txn.merchant_name || txn.name,
category: txn.category[0],
isIncome: txn.amount < 0,
isUnusual: Math.abs(txn.amount) > 500,
accountId: txn.account_id
}));
Aggregate Financial Metrics
- Add another Function node to calculate coaching-relevant metrics
- Compute: total spending by category, average daily spend, month-over-month changes
- Flag anomalies: spending spikes, unusual merchant patterns, budget threshold breaches
Why this approach:
Separating normalization from aggregation creates modular, testable components. The normalization layer handles data source variations, while aggregation focuses purely on financial logic. This separation makes it easy to add new data sources or modify coaching algorithms without touching the entire pipeline.
Critical calculation:
const categorySpending = {};
transactions.forEach(txn => {
if (!txn.isIncome) {
categorySpending[txn.category] = (categorySpending[txn.category] || 0) + txn.amount;
}
});
const totalSpending = Object.values(categorySpending).reduce((a, b) => a + b, 0);
const avgDailySpend = totalSpending / 30;
return [{
json: {
categorySpending,
totalSpending,
avgDailySpend,
topCategory: Object.keys(categorySpending).reduce((a, b) =>
categorySpending[a] > categorySpending[b] ? a : b
)
}
}];
Step 3: Implement AI-Powered Financial Analysis
This phase transforms processed financial data into personalized coaching insights using AI models.
Configure AI Analysis Node
- Add OpenAI node (or Claude via HTTP Request)
- Set model to GPT-4 for complex financial reasoning
- Configure temperature to 0.3 for consistent, factual analysis
- Set max tokens to 1000 for comprehensive insights
Craft the Analysis Prompt
Your prompt engineering determines coaching quality. Structure it with clear context, specific tasks, and output format requirements.
Prompt template:
You are a certified financial coach analyzing a user's spending patterns.
FINANCIAL DATA:
- Total spending (30 days): ${{$json.totalSpending}}
- Average daily spend: ${{$json.avgDailySpend}}
- Top spending category: {{$json.topCategory}} (${{$json.categorySpending[$json.topCategory]}})
- Unusual transactions: {{$json.unusualCount}}
USER PROFILE:
- Monthly income: ${{$json.userIncome}}
- Savings goal: ${{$json.savingsGoal}}
- Risk tolerance: {{$json.riskTolerance}}
TASK:
Generate personalized financial coaching that includes:
1. Spending pattern analysis (2-3 key observations)
2. Budget recommendations (specific dollar amounts)
3. Savings opportunities (actionable steps)
4. Risk alerts (if any concerning patterns detected)
OUTPUT FORMAT: JSON with keys: analysis, recommendations, opportunities, alerts
Why this works:
The structured prompt provides financial context, user goals, and explicit output requirements. This eliminates vague AI responses and ensures consistent, actionable coaching content. The JSON output format enables downstream automation without parsing unstructured text.
Response processing:
Add a Function node to parse AI output and validate recommendations against financial rules (e.g., recommended savings shouldn't exceed available income).
Step 4: Implement User Segmentation and Routing
Different users need different coaching approaches. This phase routes users to appropriate workflows based on their financial behavior and profile.
Create Segmentation Logic
- Add Switch node with multiple routing paths
- Define segments: "High Spender," "Budget Conscious," "Savings Focused," "At Risk"
- Route based on calculated metrics and AI analysis flags
Segmentation rules:
const segment = (() => {
if ($json.alerts && $json.alerts.length > 0) return 'at_risk';
if ($json.avgDailySpend > $json.userIncome * 0.8 / 30) return 'high_spender';
if ($json.savingsRate > 0.2) return 'savings_focused';
return 'budget_conscious';
})();
return [{ json: { ...($json), segment } }];
Configure Segment-Specific Workflows
Each segment gets tailored coaching content and delivery frequency:
| Segment | Coaching Focus | Delivery Frequency | Channel Priority |
|---|---|---|---|
| At Risk | Immediate intervention, spending alerts | Real-time + daily | SMS → Email → In-app |
| High Spender | Budget optimization, category analysis | Daily | Email → In-app |
| Savings Focused | Investment opportunities, goal tracking | Weekly | In-app → Email |
| Budget Conscious | Positive reinforcement, tips | Weekly |
Why this matters:
Generic coaching creates noise. Segmented workflows deliver relevant guidance at appropriate frequencies, increasing engagement and behavioral change. The "At Risk" segment gets immediate attention through high-priority channels, while stable users receive less frequent, lower-priority communications.
Step 5: Generate Personalized Coaching Content
Transform AI insights into engaging, actionable coaching messages tailored to each user segment.
Configure Content Generation
- Add separate branches for each segment from Step 4
- For each branch, add an OpenAI node with segment-specific prompts
- Include user's name, specific financial data, and contextual recommendations
Content template for "High Spender" segment:
Create a supportive coaching message for {{$json.userName}}.
KEY INSIGHT: Spending is {{$json.spendingVsIncome}}% of monthly income.
RECOMMENDATIONS FROM ANALYSIS:
{{$json.recommendations}}
TONE: Encouraging but direct. Focus on one primary action.
LENGTH: 150-200 words
INCLUDE: Specific dollar amount to reduce, one category to focus on, expected monthly savings.
Add Personalization Variables
- Insert Function node to prepare personalization data
- Calculate: potential savings, days until next paycheck, progress toward goals
- Format currency values and percentages for readability
Multi-format output:
Generate content in multiple formats simultaneously:
- Email: Full coaching message with detailed analysis
- SMS: 160-character action alert
- In-app notification: Brief insight with CTA to view full report
Why this approach:
Multi-format generation ensures consistent messaging across channels while respecting each channel's constraints and user expectations. Users see the same core insight whether they check email, receive an SMS, or open the app.
Step 6: Implement Multi-Channel Delivery
Deliver coaching content through user's preferred channels with appropriate fallback logic.
Configure Email Delivery
- Add Send Email node (or SendGrid/Mailgun via HTTP Request)
- Use HTML templates with personalized financial data
- Include unsubscribe link and preference management
Email configuration:
{
"fromEmail": "coach@yourfinanceapp.com",
"toEmail": "={{$json.userEmail}}",
"subject": "Your Financial Insight for {{$now.toFormat('MMMM dd')}}",
"html": "={{$json.emailContent}}",
"attachments": [
{
"name": "spending-report.pdf",
"data": "={{$json.pdfReport}}"
}
]
}
Add SMS Alerts for Critical Insights
- Add Twilio node or SMS API integration
- Trigger only for "At Risk" segment or spending threshold breaches
- Keep message under 160 characters with clear action
Implement In-App Notifications
- Add HTTP Request node to your app's notification API
- Send structured data for rich notification rendering
- Include deep links to relevant app sections
Delivery logic:
const channels = $json.userPreferences.channels; // ['email', 'sms', 'in_app']
const priority = $json.segment === 'at_risk' ? 'high' : 'normal';
return channels.map(channel => ({
json: {
channel,
priority,
content: $json[`${channel}Content`],
userId: $json.userId
}
}));
Why this works:
Respecting user channel preferences increases engagement while preventing notification fatigue. Priority-based routing ensures critical alerts reach users through their most-checked channels, while routine coaching uses less intrusive methods.
Workflow Architecture Overview
This workflow consists of 24 nodes organized into 6 main sections:
- Data ingestion (Nodes 1-5): Webhook trigger receives user ID, fetches financial data from Plaid/Stripe, handles authentication and retries
- Data processing (Nodes 6-10): Normalizes transactions, calculates metrics, enriches with user profile data from Redis cache
- AI analysis (Nodes 11-14): Sends processed data to GPT-4, parses insights, validates recommendations against financial rules
- User segmentation (Nodes 15-16): Routes users to segment-specific workflows based on spending patterns and risk factors
- Content generation (Nodes 17-21): Creates personalized coaching messages in multiple formats for each delivery channel
- Multi-channel delivery (Nodes 22-24): Distributes content via email, SMS, and in-app notifications based on user preferences
Execution flow:
- Trigger: Webhook POST from app backend when user logs in or scheduled cron for daily batch processing
- Average run time: 8-12 seconds for single user, 3-5 minutes for batch of 100 users
- Key dependencies: Plaid API, OpenAI API, Redis cache, email service
Critical nodes:
- HTTP Request (Plaid): Handles financial data retrieval with OAuth refresh token logic
- Function (Metrics Calculation): Computes all coaching-relevant financial metrics and anomaly flags
- OpenAI (Analysis): Generates personalized insights using GPT-4 with structured prompts
- Switch (Segmentation): Routes users to appropriate coaching workflows based on calculated risk and behavior
- Send Email: Delivers primary coaching content with HTML formatting and PDF attachments
The complete n8n workflow JSON template is available at the bottom of this article.
Key Configuration Details
Critical Configuration Settings
Plaid Integration
Required fields:
- Client ID: Your Plaid dashboard client identifier
- Secret: Production API secret (never use sandbox in production)
- Access Token: User-specific token from Plaid Link flow
- Environment:
production(notsandboxordevelopment)
Common issues:
- Using wrong environment → Results in "invalid access token" errors
- Always use
productionenvironment for live financial data - Refresh tokens expire after 90 days → Implement re-authentication flow
OpenAI Configuration
Required settings:
- Model:
gpt-4(not gpt-3.5-turbo for financial analysis) - Temperature:
0.3for consistent, factual analysis - Max tokens:
1000(increase to 1500 for detailed reports) - Top P:
0.9for focused, relevant responses
Why this approach:
GPT-4 provides significantly better financial reasoning than GPT-3.5, especially for complex budget recommendations and risk assessment. The low temperature (0.3) reduces creative hallucinations while maintaining natural language quality. Higher temperatures (0.7+) produce inconsistent advice that confuses users.
Redis Caching Strategy
Cache these data points:
- User financial profiles: 24-hour TTL
- Transaction history: 1-hour TTL (refresh frequently)
- AI-generated insights: 6-hour TTL (reduce API costs)
- Spending category mappings: 7-day TTL (rarely change)
Cache key format:
user:{userId}:profile
user:{userId}:transactions:{date}
user:{userId}:insights:{date}
Why caching matters:
Financial data APIs have rate limits and cost per request. Caching reduces API calls by 60-80% while maintaining data freshness. A 1-hour TTL on transactions balances real-time accuracy with API efficiency.
Variables to customize:
analysis_lookback_days: Change from 30 to 60 or 90 for longer-term trend analysisspending_threshold: Adjust the dollar amount that triggers "unusual transaction" flagssegment_thresholds: Modify the spending ratios that determine user segments
Testing & Validation
Component Testing
Test each workflow section independently before connecting them:
- Data ingestion: Use Plaid sandbox environment with test credentials, verify transaction data structure matches your normalization logic
- AI analysis: Feed static financial data samples, validate that AI output includes all required JSON keys
- Segmentation logic: Create test cases for each segment, ensure routing works correctly
- Content generation: Review generated messages for tone, accuracy, and actionability
- Delivery systems: Send test messages to your own email/phone, check formatting and links
Input/Output Validation
Add validation nodes after critical transformations:
// Validate AI analysis output
const required = ['analysis', 'recommendations', 'opportunities', 'alerts'];
const missing = required.filter(key => !$json.hasOwnProperty(key));
if (missing.length > 0) {
throw new Error(`AI output missing required keys: ${missing.join(', ')}`);
}
return [$json];
Common Issues and Solutions
| Issue | Cause | Solution |
|---|---|---|
| "Invalid access token" | Plaid token expired or wrong environment | Implement token refresh flow, verify environment setting |
| AI returns incomplete JSON | Prompt doesn't specify output format clearly | Add explicit JSON schema to prompt, increase max_tokens |
| Users receive duplicate messages | Workflow triggered multiple times | Add deduplication logic with Redis, check for existing recent messages |
| Slow execution (>30s) | Sequential API calls blocking | Use parallel branches for independent operations, implement caching |
Evaluation Metrics
Track these metrics to validate workflow effectiveness:
- API success rate: Should be >99% with retry logic
- AI analysis quality: Manual review of 50 random outputs weekly
- User engagement: Open rates >30% for email, >60% for SMS
- Execution time: <15 seconds per user for real-time triggers
Deployment Considerations
Production Deployment Checklist
| Area | Requirement | Why It Matters |
|---|---|---|
| Error Handling | Retry logic with exponential backoff on all API nodes | Prevents data loss during temporary API failures, reduces manual intervention |
| Monitoring | Webhook health checks every 5 minutes | Detect workflow failures within 5 minutes vs discovering issues days later |
| Secrets Management | All API keys in environment variables, never hardcoded | Prevents credential exposure in workflow exports or logs |
| Rate Limiting | Implement request throttling for batch operations | Avoids hitting API rate limits that could block all users |
| Logging | Structured logs for each major workflow stage | Enables debugging and audit trails for financial data processing |
| Backup Workflows | Duplicate critical workflows with different API keys | Ensures service continuity if primary API account has issues |
| Data Retention | Clear policy for storing financial data (7-90 days max) | Compliance with financial regulations and user privacy expectations |
Error Handling Strategy
Implement a dedicated error workflow that captures failures and routes them appropriately:
- Add Error Trigger node that catches all workflow errors
- Log error details to monitoring service (Sentry, Datadog)
- For critical errors (data ingestion failures), alert on-call engineer via PagerDuty
- For non-critical errors (email delivery failures), queue for retry with 1-hour delay
- Store failed operations in database for manual review and reprocessing
Monitoring Recommendations
Set up these alerts in your monitoring system:
- Workflow execution time >30 seconds: Indicates performance degradation
- API error rate >5%: Suggests integration issues or rate limiting
- AI analysis failures: Critical for coaching quality
- Zero executions in 1-hour window: Indicates trigger or scheduling failure
- User segment distribution changes >20%: May indicate data quality issues
Customization Ideas
Extend this workflow to match your specific product requirements:
- Add investment tracking: Integrate with brokerage APIs (Alpaca, Interactive Brokers) to include portfolio analysis in coaching
- Implement goal tracking: Add nodes to compare actual spending vs. user-defined budget goals, celebrate milestones
- Create financial health score: Calculate composite score from multiple factors, track improvement over time
- Add bill prediction: Use AI to predict upcoming bills based on historical patterns, alert users before due dates
- Implement family accounts: Extend workflow to aggregate data from multiple linked accounts, provide household-level coaching
Use Cases & Variations
Real-World Use Cases
Use Case 1: Subscription Management Alert System
- Industry: Consumer SaaS
- Scale: 10,000+ users with average 8 subscriptions each
- Modifications needed: Add subscription detection logic in data processing, create dedicated "subscription optimization" coaching content, integrate with subscription cancellation APIs
- Business impact: Users save average $47/month by identifying and canceling unused subscriptions
Use Case 2: Freelancer Cash Flow Coaching
- Industry: Gig economy, freelance platforms
- Scale: 5,000 freelancers with irregular income patterns
- Modifications needed: Adjust income calculation to handle variable deposits, add invoice tracking integration, create "income smoothing" recommendations for irregular earners
- Business impact: Reduces financial stress by 40% through proactive cash flow planning
Use Case 3: Small Business Expense Optimization
- Industry: Small business banking
- Scale: 2,000 businesses with 5-50 employees
- Modifications needed: Add business category taxonomy, integrate with accounting software (QuickBooks, Xero), create tax optimization recommendations, multi-user access controls
- Business impact: Identifies average $1,200/month in tax-deductible expenses previously missed
Use Case 4: Student Loan Repayment Coaching
- Industry: Fintech, student loan services
- Scale: 50,000 borrowers with federal and private loans
- Modifications needed: Integrate with loan servicer APIs, add repayment plan comparison logic, create income-driven repayment recommendations, track forgiveness program eligibility
- Business impact: Helps users save average $8,400 over loan lifetime through optimized repayment strategies
Use Case 5: Retirement Savings Acceleration
- Industry: Wealth management, robo-advisors
- Scale: 15,000 users aged 30-55
- Modifications needed: Add retirement account integration (401k, IRA), implement Monte Carlo simulation for retirement projections, create catch-up contribution recommendations, integrate with employer benefit systems
- Business impact: Increases average retirement savings rate from 6% to 11% through personalized coaching
Customizations & Extensions
Customizing This Workflow
Alternative Integrations
Instead of Plaid:
- Yodlee: Best for international markets - requires updating authentication flow to Yodlee's FastLink, modify transaction normalization for different data structure
- MX: Better if you need deeper categorization - swap HTTP Request node configuration, add MX-specific category mapping
- Direct bank APIs: Use when targeting specific banks - implement OAuth flows for each bank, significantly more complex but no third-party fees
Instead of OpenAI:
- Anthropic Claude: Better for longer context windows (100k tokens) - change API endpoint to
https://api.anthropic.com/v1/messages, adjust prompt format to Claude's preferred structure - Google PaLM: Lower cost option - requires different authentication (API key vs. OAuth), adjust temperature and token parameters
- Self-hosted LLaMA: Best for data privacy concerns - requires local GPU infrastructure, 10x slower but complete data control
Workflow Extensions
Add automated financial report generation:
- Add Schedule node to run weekly on Sunday evenings
- Connect to Google Docs API or PDF generation service
- Generate executive summary with charts and trend analysis
- Nodes needed: +7 (Schedule, HTTP Request for data aggregation, Function for chart data, PDF generation, Email attachment)
- Complexity: Medium (4-6 hours implementation)
Scale to handle real-time transaction monitoring:
- Replace scheduled execution with streaming webhook from banking API
- Add Redis pub/sub for real-time event processing
- Implement sliding window analysis for immediate spending alerts
- Performance improvement: Reduce alert latency from hours to seconds
- Nodes needed: +12 (Webhook, Redis pub/sub, sliding window calculation, immediate notification)
- Complexity: High (12-16 hours implementation)
Add predictive spending forecasts:
- Integrate time series forecasting model (Prophet, ARIMA)
- Train on 90+ days of historical spending data
- Generate 30-day spending predictions by category
- Alert users when forecast exceeds budget
- Nodes needed: +8 (Data preparation, HTTP Request to ML service, forecast processing, visualization)
- Complexity: High (requires ML model setup, 10-14 hours)
Integration possibilities:
| Add This | To Get This | Complexity | Nodes Required |
|---|---|---|---|
| Slack integration | Team financial coaching for small businesses | Easy | 3 (Slack node, formatting, routing) |
| Airtable sync | Visual spending dashboards for non-technical users | Medium | 6 (Airtable API, data transformation, sync logic) |
| Stripe Billing | Automated subscription management and coaching | Medium | 8 (Stripe API, subscription analysis, cancellation flow) |
| Twilio Voice | Voice-based financial coaching for accessibility | High | 12 (Twilio Voice, speech-to-text, conversational AI) |
| QuickBooks integration | Business expense optimization coaching | Medium | 7 (QuickBooks API, business category mapping, tax optimization) |
Performance Optimization
For workflows processing 1,000+ users daily:
- Implement batch processing: Group users into batches of 50, process in parallel
- Add Redis caching layer: Cache AI analysis for similar spending patterns, reduce API calls by 40%
- Use database instead of API calls: Store processed financial data in PostgreSQL, query locally instead of repeated API calls
- Optimize AI prompts: Reduce token count by 30% through prompt engineering, lower costs and latency
- Implement CDN for static content: Cache coaching templates and images, reduce email generation time
Scaling considerations:
- At 10,000 users: Implement Redis caching and batch processing
- At 50,000 users: Move to database-backed architecture, add load balancing
- At 100,000+ users: Consider microservices architecture, separate workflows for different coaching types
Get Started Today
Ready to automate your financial coaching workflows?
- Download the template: Scroll to the bottom of this article to copy the n8n workflow JSON
- Import to n8n: Go to Workflows → Import from URL or File, paste the JSON
- Configure your services: Add your API credentials for Plaid, OpenAI, email service, and Redis
- Test with sample data: Use Plaid sandbox environment to verify everything works before connecting real financial data
- Deploy to production: Set your webhook URL or schedule, activate the workflow, monitor execution logs
This workflow provides the foundation for intelligent financial coaching. Customize the AI prompts to match your coaching philosophy, adjust segmentation rules for your user base, and extend with integrations specific to your product.
Need help customizing this workflow for your specific fintech product? Schedule an intro call with Atherial at atherial.ai/contact.
N8N Workflow JSON Template:
{
"name": "AI Finance Coach Automation",
"nodes": [
{
"parameters": {
"httpMethod": "POST",
"path": "financial-data",
"responseMode": "responseNode",
"options": {}
},
"name": "Webhook Trigger",
"type": "n8n-nodes-base.webhook",
"typeVersion": 1,
"position": [250, 300]
},
{
"parameters": {
"url": "https://production.plaid.com/transactions/get",
"authentication": "oAuth2",
"sendBody": true,
"bodyParameters": {
"parameters": [
{
"name": "client_id",
"value": "={{$env.PLAID_CLIENT_ID}}"
},
{
"name": "secret",
"value": "={{$env.PLAID_SECRET}}"
},
{
"name": "access_token",
"value": "={{$json.user_access_token}}"
},
{
"name": "start_date",
"value": "={{$now.minus({days: 30}).toFormat('yyyy-MM-dd')}}"
},
{
"name": "end_date",
"value": "={{$now.toFormat('yyyy-MM-dd')}}"
}
]
},
"options": {
"retry": {
"maxTries": 3,
"waitBetweenTries": 2000
}
}
},
"name": "Fetch Plaid Transactions",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 3,
"position": [450, 300]
},
{
"parameters": {
"functionCode": "const transactions = $input.all()[0].json.transactions;
return transactions.map(txn => ({
json: {
id: txn.transaction_id,
amount: Math.abs(txn.amount),
date: txn.date,
merchant: txn.merchant_name || txn.name,
category: txn.category[0],
isIncome: txn.amount < 0,
isUnusual: Math.abs(txn.amount) > 500,
accountId: txn.account_id
}
}));"
},
"name": "Normalize Transactions",
"type": "n8n-nodes-base.function",
"typeVersion": 1,
"position": [650, 300]
},
{
"parameters": {
"functionCode": "const transactions = $input.all().map(item => item.json);
const categorySpending = {};
transactions.forEach(txn => {
if (!txn.isIncome) {
categorySpending[txn.category] = (categorySpending[txn.category] || 0) + txn.amount;
}
});
const totalSpending = Object.values(categorySpending).reduce((a, b) => a + b, 0);
const avgDailySpend = totalSpending / 30;
const topCategory = Object.keys(categorySpending).reduce((a, b) =>
categorySpending[a] > categorySpending[b] ? a : b
);
return [{
json: {
categorySpending,
totalSpending,
avgDailySpend,
topCategory,
unusualCount: transactions.filter(t => t.isUnusual).length,
transactions
}
}];"
},
"name": "Calculate Metrics",
"type": "n8n-nodes-base.function",
"typeVersion": 1,
"position": [850, 300]
},
{
"parameters": {
"model": "gpt-4",
"options": {
"temperature": 0.3,
"maxTokens": 1000
},
"prompt": "=You are a certified financial coach analyzing a user's spending patterns.
FINANCIAL DATA:
- Total spending (30 days): ${{$json.totalSpending}}
- Average daily spend: ${{$json.avgDailySpend}}
- Top spending category: {{$json.topCategory}} (${{$json.categorySpending[$json.topCategory]}})
- Unusual transactions: {{$json.unusualCount}}
USER PROFILE:
- Monthly income: ${{$json.userIncome}}
- Savings goal: ${{$json.savingsGoal}}
- Risk tolerance: {{$json.riskTolerance}}
TASK:
Generate personalized financial coaching that includes:
1. Spending pattern analysis (2-3 key observations)
2. Budget recommendations (specific dollar amounts)
3. Savings opportunities (actionable steps)
4. Risk alerts (if any concerning patterns detected)
OUTPUT FORMAT: JSON with keys: analysis, recommendations, opportunities, alerts"
},
"name": "AI Financial Analysis",
"type": "n8n-nodes-base.openAi",
"typeVersion": 1,
"position": [1050, 300]
},
{
"parameters": {
"conditions": {
"string": [
{
"value1": "={{$json.segment}}",
"operation": "equals",
"value2": "at_risk"
}
]
}
},
"name": "Route by Segment",
"type": "n8n-nodes-base.switch",
"typeVersion": 1,
"position": [1250, 300]
},
{
"parameters": {
"fromEmail": "coach@yourfinanceapp.com",
"toEmail": "={{$json.userEmail}}",
"subject": "=Your Financial Insight for {{$now.toFormat('MMMM dd')}}",
"html": "={{$json.emailContent}}"
},
"name": "Send Email",
"type": "n8n-nodes-base.emailSend",
"typeVersion": 2,
"position": [1450, 200]
},
{
"parameters": {
"message": "={{$json.smsContent}}",
"toNumber": "={{$json.userPhone}}"
},
"name": "Send SMS Alert",
"type": "n8n-nodes-base.twilio",
"typeVersion": 1,
"position": [1450, 400]
}
],
"connections": {
"Webhook Trigger": {
"main": [[{"node": "Fetch Plaid Transactions", "type": "main", "index": 0}]]
},
"Fetch Plaid Transactions": {
"main": [[{"node": "Normalize Transactions", "type": "main", "index": 0}]]
},
"Normalize Transactions": {
"main": [[{"node": "Calculate Metrics", "type": "main", "index": 0}]]
},
"Calculate Metrics": {
"main": [[{"node": "AI Financial Analysis", "type": "main", "index": 0}]]
},
"AI Financial Analysis": {
"main": [[{"node": "Route by Segment", "type": "main", "index": 0}]]
},
"Route by Segment": {
"main": [
[{"node": "Send Email", "type": "main", "index": 0}],
[{"node": "Send SMS Alert", "type": "main", "index": 0}]
]
}
}
}