Automate Bank & Credit Card Spend Extraction with n8n & Gmail

Opening Problem Statement

Meet Alice, a finance manager at a consulting firm who is buried under mountains of bank and credit card email statements every month. She’s responsible for accounting precise spend details, reconciling invoices, and entering all transaction data into Google Sheets manually. Each invoice or payment email contains lots of details like transaction dates, merchant names, amounts, and categories, but parsing it takes hours and opens room for human mistakes. On average, Alice spends 6-8 hours monthly extracting and logging these expenses, delaying financial reporting and risking errors that impact budgeting accuracy.

This cumbersome manual task is exactly the pain point solved by the “Extract spend details (template)” n8n workflow. It drastically reduces Alice’s manual workload by automating the parsing and organizing of spend details directly from Gmail invoice and payment emails, applying intelligent AI extraction, and pushing structured data into Google Sheets.

What This Automation Does

This n8n workflow listens to new emails labeled as invoices or payments in Gmail and extracts detailed spend information with minimal intervention. When it runs, it accomplishes:

• Fetches invoice and payment emails from specifically labeled Gmail inbox folders every minute.
• Extracts PDF attachments (locked by a password) and email HTML content for spend details.
• Differentiates between emails containing multiple payment infos, single payment info, or invoices, routing each properly.
• Leverages Google Gemini and Groq AI language models for advanced parsing of unstructured email content into structured transaction records.
• Automatically formats spend details into predefined schemas including date, service, details, amount, category, currency, and card.
• Appends all extracted, parsed, and verified spend data into a Google Sheets document for bookkeeping and further analysis.

By automating this complex multi-step extraction and logging process, Alice saves up to several hours each month and reduces error risks by standardizing data entry.

Prerequisites 

• 📧 Gmail account with labeled email invoices and payment notification emails.
• 🔐 Gmail OAuth2 credentials configured in n8n to access and read labeled emails.
• ⏱️ n8n workflow automation platform account (cloud or self-hosted – consider Hostinger self-hosting).
• 🧠 Google Gemini (PaLM) API and Groq API accounts for AI model integration.
• 📊 Google Sheets with defined sheets/schemas and OAuth2 credentials for appending processed data.

Step-by-Step Guide

Step 1: Set up Gmail Labels and Filters

Log into your Gmail account and create labels for invoice and payment emails (e.g., Invoice and Payment). Use Gmail filters to automatically assign these labels to incoming emails from banks or credit card services that send your statements or spend notifications.

This ensures relevant emails are organized for n8n triggers.

Common Mistake: Forgetting to apply Gmail filters will result in missing emails in your workflow.

Step 2: Configure n8n Gmail Trigger Nodes

In n8n, add two Gmail Trigger nodes named Get invoice and Get payment. Set each to poll every minute and filter emails by the label IDs assigned to invoices and payments respectively.

Enable Download Attachments option to fetch invoice PDFs attached.

This sets up the email input for the automation.

Step 3: Extract Data from PDFs

Connect each Gmail Trigger node to an Extract from File (PDF) node configured with the password “E223706995” to unlock and parse secured PDFs attached to the emails.

The node extracts text content from PDFs assigned to the “attachment_0” binary property.

Visual: You should see text content extracted or the node continues without error if no attachments are found.

Step 4: Prepare Email Content Variables

Use Set nodes to organize and assign structured fields like html, subject, date, text, label, and from from emails and extracted file contents for further processing.

This normalizes different email data into a consistent format.

Step 5: Route Email Types Using Switch Node

Add a Switch node to route emails based on sender address to identify three categories:

• Invoices: Emails that are typical invoices, not single or multiple payments.
• Multiple payment info emails: Like daily spend notifications having multiple payment entries.
• One payment info emails: Instant spend notification emails containing only one payment info.

This branching handles different parsing logic.

Step 6: Extract HTML Spend Details

For invoices and multiple payment info emails, connect the HTML node to extract specific HTML elements that contain spend tables using CSS selectors like “.spend-table”.

Feed the extracted array of spends into Split Out node to handle each spend record separately.

Step 7: Set Spend Record Data

Use Set nodes to assign spend attribute values like email_date, email_subject, email_content, and a type indicator (email_type) to track which category the spend belongs to.

Step 8: Merge Processed Data Streams

Merge all prepared data streams into a single flow using a Merge node to prepare for AI parsing.

Step 9: Call AI Language Models for Parsing

Use two LangChain AI nodes (Extract details with Google Gemini model and Extract details1 with Groq model) to parse the email content and extract structured transaction details compliant with the manual JSON schema.

They receive prompts requesting detailed extraction of transaction date, amount, merchant, category, currency, and more for accounting purposes.

Step 10: Use Structured Output Parsers

Each LangChain AI node output is sent through a Structured Output Parser node with a detailed JSON schema defining transaction data fields and data types to guarantee consistent formatted data.

Step 11: Append Parsed Spend Data to Google Sheets

Append the parsed transactions to a specific Google Sheets document using Google Sheets nodes, mapping fields such as date, amount, service, details, payment, category, and currency.

This creates a live, accurate ledger of bank and credit card spend details. The workflow retries on failure, ensuring data integrity.

Customizations 

1. Change PDF Password in Extraction Nodes: In the Extract invoice and Extract payment nodes, modify the password parameter to match your secured PDF attachments.

   This allows parsing different secured invoice file formats.

2. Add More Gmail Labels for Other Card Services: Update the Get invoice and Get payment Gmail Trigger nodes’ label ID filters to include more card services.

   Supports multiple credit cards or banks.

3. Modify Spend CSS Selector: Change the .spend-table CSS selector in the HTML node to match your service’s spend detail HTML structure.

   This ensures accurate extraction from email HTML content.

4. Edit AI Output Parsing Schema: Adjust the JSON schema in Structured Output Parser nodes to add or remove fields or change categories.

   Tune the output to your bookkeeping needs.

5. Customize AI Prompt: Update prompt messages in the LangChain Extract details nodes to refine extraction instructions or target specific transaction data.

   Improves accuracy and relevance.

Troubleshooting 

1. Problem: “No emails found with label ID”
   
   Cause: Gmail labels or filter setup incorrect or label IDs changed.
   
   Solution: Verify and update label IDs in Gmail Trigger nodes under filters > labelIds exactly to your Gmail setup. Refresh credentials if necessary.
2. Problem: “PDF extraction failed or no attachments”
   
   Cause: PDF is password protected with a different password or attachment property name mismatch.
   
   Solution: Check the PDF password in Extract from File nodes and ensure attachment property name is “attachment_0” as set in Gmail nodes.
3. Problem: “AI output parse errors or inconsistent data”
   
   Cause: AI prompt or schema mismatch causes incorrect parsing.
   
   Solution: Modify AI node prompts and validate JSON schema to align with actual email content. Test step-by-step with sample emails.
4. Problem: “Workflow fails to append to Google Sheets”
   
   Cause: OAuth2 credentials expired or incorrect sheet name/document ID.
   
   Solution: Reconnect Google Sheets OAuth2 credentials and verify the target sheet name and document ID in the Google Sheets nodes.

Pre-Production Checklist 

• Confirm Gmail labels and filters correctly tag invoice and payment emails.
• Verify Gmail OAuth2 credentials in n8n allow reading labeled emails and downloading attachments.
• Test PDF extraction nodes with actual locked invoice/payment PDFs.
• Validate AI nodes output by running test emails and checking parsed data matches schema.
• Check Google Sheets OAuth2 credentials and spreadsheet permissions.
• Backup Google Sheet data prior to deployment for rollback safety.

Deployment Guide

Activate the workflow in n8n by toggling it active. Ensure the Gmail triggers run at the intended polling frequency (every minute).

Monitor workflow executions initially to catch errors using n8n’s execution logs.

Set up alerts or notifications if key nodes fail to guarantee data completeness.

FAQs

Q: Can this workflow work with Outlook or other email providers?
A: This workflow specifically uses Gmail Trigger nodes that require Gmail labels and OAuth2. You would need to replace these triggers with an Outlook or IMAP trigger and adjust email parsing accordingly.

Q: Does AI parsing consume a lot of API credits?
A: The Google Gemini and Groq AI models use paid API calls, so expect cost based on usage volume. Monitor usage carefully.

Q: Is my financial data secure?
A: Data security depends on your n8n environment and credential management. Use environment-specific credentials and encrypt sensitive data where possible.

Q: Can this handle hundreds of emails daily?
A: Yes, the workflow is designed for frequent polling and scalable AI parsing, but consider API rate limits and plan accordingly.

Conclusion

By implementing this n8n workflow, Alice now automates the tedious extraction of bank and credit card spend details from her Gmail inbox. She saves several hours a month, reduces manual errors, and gains consistent, structured financial data for her reports. This automation brings peace of mind and better control over spend tracking.

Next, you could explore automations to categorize transactions dynamically, generate monthly spend reports automatically, or integrate alerts for unusual expenses—all built using n8n’s powerful automation platform.

Give it a try, and reclaim your time from manual financial data entry!