Extracting Credit Card Spend Details with n8n and AI Automation

1. Opening Problem Statement

Meet Hana, a freelance accountant who manages multiple corporate credit cards for her clients. Every month, Hana receives dozens of emails containing credit card spend notifications and payment confirmations, presented in various formats: PDFs of invoices, payment receipts, and HTML-formatted transaction emails. Manually extracting accurate transaction details — including dates, merchant names, amounts, categories, and payment methods — from these diverse sources wastes Hana around 4 hours weekly. Errors creep in due to inconsistent email formats, multiple payment entries bundled in one email, and password-protected attachments requiring manual opening. This slows Hana down and risks mistakes in client bookkeeping, costing both time and money.

2. What This Automation Does

This n8n workflow transforms Hana’s tedious email bookkeeping into an automated, reliable process. Once set up, it:

• Monitors Gmail labels for credit card invoices and payment emails, pulling new messages every minute.
• Extracts attachments and email body content — including password-protected PDFs — to retrieve spend and payment details.
• Uses AI-powered parsing (Google Gemini or Groq chat models) to accurately interpret complex spend entries from various email formats, including multiple payment info in one email or single payment notifications.
• Structures extracted data into a standardized schema including date, service name, transaction details, amount, currency, category, and credit card used.
• Appends the processed data into a Google Sheets spreadsheet, ready for quick review and client accounting.

Overall, Hana saves at least 4 hours weekly, eliminates manual transcription errors, and gains confidence that her bookkeeping captures every detail accurately.

3. Prerequisites ⚙️

• Gmail account with labels set to categorize invoice and payment emails 📧
• Google Sheets account to store structured spend data 📊
• n8n automation platform account (cloud or self-hosted) 🔌
• Google Gemini (PaLM) or Groq AI API credentials for natural language and PDF parsing 🔐
• Passwords for any encrypted PDF invoices used in your emails (e.g., “E223706995”) 🔑

Optional Self-hosting for n8n: Host n8n yourself for more control and data privacy.

4. Step-by-Step Guide

Step 1: Create Gmail Labels for Invoice and Payment Emails

In Gmail, create labels such as “Credit Card Invoice” and “Credit Card Payment”. Then use Gmail filters to automatically assign these labels to incoming relevant emails from your credit card companies.

This allows the n8n Gmail Trigger nodes to listen specifically to these labeled emails.

Tip: Use consistent email addresses or subject filters in Gmail’s filter settings for better accuracy.

Step 2: Add Gmail Trigger Nodes to n8n Workflow

In n8n, add two Gmail Trigger nodes named “Get invoice” and “Get payment”.

• Set “Label IDs” to match your Gmail labels (e.g., “Label_7885838942566773656” for invoices).
• Enable “Download Attachments” so PDFs are accessible.
• Set polling interval to “every minute” for near real-time processing.
• Connect your Gmail OAuth2 credentials with proper permissions.

These nodes start the workflow whenever new labeled emails arrive.

Step 3: Extract PDF Attachments from Emails

After each Gmail Trigger node, add an Extract From File node configured for PDF extraction named respectively “Extract invoice” and “Extract payment”.

• Set the binary property name to “attachment_0” to target the first attachment.
• Enter the PDF password if the attachment is encrypted (e.g., “E223706995”).
• Configure the node to continue on errors to handle emails without attachments gracefully.

This extracts text content from encrypted PDFs.

Step 4: Prepare Email Content for Further Processing

Use Set nodes to structure the email data for each path (invoice, payment, or multiple payment info).

• Assign properties such as email date, subject, HTML content, plain text content, label IDs, and sender information.
• This standardized format streamlines downstream processing.

Example variable assignment in “Set data 0”:

{
  email_date: {{$node["Switch"].item.json.date}},
  email_subject: {{$node["Switch"].item.json.subject}},
  email_content: $json.spend || $json.text || $json.html,
  email_type: 0
}

Step 5: Route Emails by Sender with a Switch Node

Add a Switch node to categorize emails based on their “from” addresses into three outputs:

• Multiple payment info in one mail (e.g., credit card daily spend summaries)
• One payment info in one mail (instant payment notifications)
• Invoices (other types)

This routing enables different parsing techniques depending on email type.

Step 6: Extract HTML Spend Details for Multi-Payment Emails

For emails containing multiple spends, use an HTML node:

• Set to extract content from CSS selectors like “.spend-table” that wrap spend items.
• Use a Split Out node to process each spend entry separately.
• Then use a Set node to format data before sending to AI parsing.

Step 7: Parse Spend and Payment Details Using AI Language Models

This workflow uses two AI nodes for natural language processing:

• Google Gemini Chat Model: Processes emails with one spend/payment entry.
• Groq Chat Model: Alternative AI engine to parse complex or multi-record emails.

Both AI nodes use a Structured Output Parser node to enforce a JSON schema output defining fields such as date, amount, service, category, currency, and card.

Prompt example for AI:

Please analyze the following email to extract transaction details for bookkeeping purposes.

Extract transaction date, amount, merchant name, and categorize the transaction.
Format the output according to the specified schema: date, amount, category, currency, etc.

Step 8: Append Extracted Data into Google Sheets

Using the Google Sheets node called “Send” or “Send1”:

• Map structured fields from the AI parser to spreadsheet columns such as date, amount, service, category, currency, payment card, and source identifier.
• Append data to a specified sheet within a Google Spreadsheet document.
• Enable retry on failure for robustness.

This step makes the data easy to review and integrate with client accounting systems.

Step 9: Add Sticky Notes to Document Your Workflow

Add Sticky Note nodes at strategic points with documentation about each workflow section, including instructions to set up Gmail labels and explanations of email source categories.

This helps maintain the workflow and onboard new users faster.

5. Customizations ✏️

• Adjust PDF password: In both Extract From File nodes (invoice and payment), change the password field to match your encrypted PDFs.
• Add more email senders for Switch routing: Update conditions in the Switch node to handle additional sender email addresses for varying types of transaction emails.
• Modify AI Output Schema: Edit the JSON schema in Structured Output Parser nodes to include custom fields or categories specific to your bookkeeping needs.
• Change Google Sheets target sheet: In Send nodes, change the document ID or sheet name to match your own Google Sheets structure.
• Enhance HTML extraction: Customize the CSS selector in the HTML node for different email formats that structure spend data uniquely.

6. Troubleshooting 🔧

Problem: “No attachments found in email”

Cause: Email doesn’t have PDF attachments or attachment name differs.

Solution: Confirm Gmail filters are correctly labeling emails with attachments. Check “Download Attachments” option is enabled in Gmail Trigger nodes.

Problem: “AI parsing output does not match schema”

Cause: AI prompt or schema mismatch or malformed email content.

Solution: Review and simplify AI prompts. Adjust JSON schema in Structured Output Parser nodes. Test parsing with sample emails.

Problem: “Google Sheets append fails”

Cause: Incorrect sheet name or document authorization problem.

Solution: Verify spreadsheet URL and sheet ID are correct. Re-authenticate Google Sheets credentials.

7. Pre-Production Checklist ✅

• Verify Gmail labels exist and automatically filter relevant emails.
• Test Gmail triggers by sending sample labeled emails.
• Ensure PDF attachments are accessible and password correct in extraction nodes.
• Test AI parsing nodes with sample email texts.
• Confirm Google Sheets connection and correct spreadsheet permissions.
• Make a test run and inspect logged data for completeness and accuracy.

8. Deployment Guide

Activate the workflow by turning on the n8n automation once configured.

Monitor the first few runs using the n8n execution logs to ensure emails are received and parsed properly.

Regularly review Google Sheets data to spot any parsing anomalies early.

Use n8n’s retry features on critical nodes like Google Sheets appends to ensure data integrity.

9. FAQs

Q: Can I use another AI provider instead of Google Gemini or Groq?
A: Yes, this workflow can be adapted to other AI language model nodes compatible with n8n, but you will need to adjust prompts and output parsing accordingly.

Q: Does this consume many API credits?
A: Since AI models process email content per transaction, API usage depends on email volume, but this workflow batches multiple spends in one request when possible to reduce calls.

Q: Is my data secure?
A: Your data security depends on your accounts and AI providers. Using self-hosted n8n and secure credentials management reduces risk.

Q: Can this handle volume spikes?
A: The workflow polls Gmail every minute and processes emails as they come. Large volumes may need scaling your n8n instance and optimizing AI calls.

10. Conclusion

You’ve created a powerful automated system that extracts detailed credit card spend and payment information from multiple email formats using n8n, Gmail, AI language models, and Google Sheets. This saves you hours each week, virtually eliminates manual errors, and streamlines client bookkeeping with standardized data output.

Next, consider extending the workflow to categorize expenses more granularly with custom AI prompts or integrate with accounting software for end-to-end automation.

Enjoy your new time-saving and error-reducing credit card spend detail extractor!