This n8n AI Agent Catches Credit Card Fraud BEFORE It Happens!

Key Concepts

AI Agent, Expense Tracking Automation, Credit Card Transaction Monitoring, Anomaly Detection, Large Language Models (LLMs), Pinecone Vector Database, Google Sheets Integration, Email Analysis, Risk Assessment, Structured Output Parsing, N8N Automation Platform.

AI Agent for Automated Expense Tracking and Credit Card Monitoring

The video demonstrates how to build an AI agent using N8N to automate the tedious process of manually tracking expenses and monitoring credit card transactions. The AI agent monitors email, extracts transaction details, analyzes spending patterns, flags unusual activity, searches for unknown merchants, assigns risk labels, and outputs everything in a structured Google Sheet.

1. Workflow Overview and Demo

• The AI agent automates credit card transaction monitoring by watching for email notifications.
• Example: A Chase transaction email for $119.99 to Canva is used as a demo.
• The workflow uses a Gmail trigger to constantly monitor the inbox.
• The AI agent analyzes the email, extracts information, and updates a Google Sheet.
• It uses the Chat-GPT model and a Pinecone vector database (containing past transactions) to analyze transactions and detect anomalies.
• The agent searches the web for merchant information and adds labels to transactions (e.g., "Anomaly Detected").
• Anomalies are also recorded in a separate "anomaly sheet" with justifications.

2. N8N Node Breakdown and Customization

• Gmail Trigger: Monitors the inbox for unread emails, pulling every minute. This can be customized to run at specific times (e.g., daily at 8:00 AM).
• Set Field Node: Extracts the email ID, body, and subject from the email.
• AI Node with Output Parser:
  • Uses GPT-4 Mini (or other LLMs) to analyze the email content.
  • A system message instructs the AI to act as an intelligent email analyzer specializing in credit card payment emails.
  • The AI classifies the email as a credit card transaction with high, medium, or low confidence, providing a reason.
  • Example: The system is designed to monitor credit card emails from Chase, American Express, and Bank of America, which are triggered by transaction alerts set up in the user's online account.
  • The structured output parser extracts confidence level and reason.
• If Node: Filters transactions based on confidence (high or medium) to focus on likely credit card transactions.
• Google Label Node: Adds a "CC Transaction" label (yellow) to identified credit card transactions.
• Information Extractor Node:
  • Extracts transaction details (amount, date, merchant, payment method, last four digits of the card) using a predefined schema.
  • The schema can be customized to include additional details like transaction location.
  • Example: The video mentions using a custom GPT within the N8N community to generate the schema.
• Google Sheets Node: Appends the extracted transaction details to a Google Sheet for monitoring.
  • The Google Sheet includes columns for amount, transaction date, merchant, card last four, and payment method.
• AI Agent Node (Anomaly Detection and Risk Assessment):
  • Uses GPT-4, Cloud, or other LLMs.
  • Has access to tools like SERP API, Wikipedia, Calculator, and a structured output parser.
  • Crucially, it uses a Pinecone vector database containing historical credit card statements.
  • The AI agent compares new transactions to historical spending patterns to detect anomalies.
  • System Message: Instructs the AI agent to analyze transactions, detect anomalies, gather merchant information, and provide a structured risk assessment.
  • Tool Usage Rules: Specifies when to use each tool (Pinecone for anomaly detection, Wikipedia/SERP API for merchant search).
  • Example Workflow: Provides an example of how the AI agent should process a new transaction, including the actions taken and the structured JSON output.

3. Pinecone Vector Database Setup

• A separate workflow is used to insert historical transaction data into the Pinecone vector database.
• Process:
  1. Download credit card statements and clean the data (e.g., put it in a Google Sheet).
  2. Use a manual trigger to initiate the data loading process.
  3. Download the Google Sheet containing the transaction data.
  4. Insert the documents into the Pinecone index.
  5. Use an embedding model (e.g., text-embedding-3-small from OpenAI) to vectorize the data.
  6. Configure a default data loader to handle binary data (e.g., Google Sheets, PDFs).
  7. Chunk the data for efficient processing (e.g., chunk size of 500 with 50 overlap).
• This process only needs to be done once to populate the database with historical data.

4. AI Agent Prompting and Tool Usage

• The AI agent's prompt is crucial for instructing it on when to use each tool.
• Pinecone Vector Database: Used for anomaly detection by comparing new transactions to historical spending.
• Wikipedia/SERP API: Used for merchant lookup to validate the legitimacy of the merchant.
• Calculator: Used for spending pattern calculations.
• The AI agent outputs a structured JSON format with risk level, anomaly summary, merchant info, and merchant category.

5. Risk Assessment and Alerting

• A switch node routes transactions based on risk level (high, medium, low).
• High-Risk Transactions:
  • Trigger immediate alerts via WhatsApp, SMS (Twilio), or email.
  • Add a "CC Anomaly Detected" label (red) to the email.
  • Record the transaction in the "anomaly sheet" in Google Sheets, including a justification from the AI agent.
• Low/Medium-Risk Transactions:
  • Can be added to a separate Google Sheet or AirTable for later review.
• Example: The demo uses a Canva transaction (which is not fraudulent) to demonstrate the labeling process, even though the AI agent correctly identifies it as low risk.

6. Structured Output Parsing

• The structured output parser is used to define the format of the AI agent's output.
• It specifies the parameters to be extracted (e.g., risk level, anomaly summary, merchant info).
• This allows for easy integration with other nodes and systems.

7. Conclusion

The AI agent provides a powerful and customizable solution for automating expense tracking and credit card monitoring. By leveraging LLMs, vector databases, and N8N's automation capabilities, users can significantly reduce the time and effort required to manage their finances and detect potential fraud. The key takeaways are the importance of clear prompting, proper tool selection, and structured output parsing for building effective AI agents.