Building a Python-based Credit Card Statement Analyzer with Gmail Integration

As a software engineer passionate about personal finance and automation, I recently developed a Python application that revolutionizes how we analyze credit card statements. This tool automatically fetches statements from Gmail, extracts data from PDFs, and generates comprehensive spending insights through interactive visualizations.

Github Link: https://github.com/Nishad94/financial-analyzer

🎯 Key Features

1. Automated Email Processing

• Integrates with Gmail API to search for credit card statements

• Uses fuzzy matching with 80%+ confidence threshold for reliable email identification

• Supports multiple banks and credit card types

• Handles both password-protected and regular PDF statements

2. Smart Data Extraction

• Processes PDF statements using advanced text extraction

• Handles encrypted PDFs securely with upfront password collection

• Extracts key financial data like transactions, due dates, and credit limits

• Maintains data privacy by processing everything locally

3. Comprehensive Analysis

• Generates spending pattern analysis

• Tracks credit utilization over time

• Creates payment history visualizations

• Provides interactive HTML dashboards

🧠 Use cases

1. Ask it to find credit card bank statements in your Gmail account based on the search term and time period you provide.

2. It will create beautiful charts and dashboards that display

• Monthly spending

• Monthly credit utilization ratio

• Total purchases over the selected time period

• Median monthly spending

• MORE TO FOLLOW!!

🛠️ Technical Implementation

The project is built using Python and leverages several key technologies:

# Key dependencies
- fuzzywuzzy[speedup]>=0.18.0  # For fuzzy string matching
- pdfplumber & PyPDF2          # For PDF processing
- Gmail API                    # For email integration

Fuzzy Matching Algorithm

The application uses a sophisticated fuzzy matching system to identify relevant emails:

scores = [
    fuzz.ratio(subject_lower, search_lower),
    fuzz.partial_ratio(subject_lower, search_lower), 
    fuzz.token_sort_ratio(subject_lower, search_lower),
    fuzz.token_set_ratio(subject_lower, search_lower)
]
best_score = max(scores)
return best_score >= 80  # 80% threshold

Security First Approach

• OAuth2 authentication for Gmail API

• Secure password handling using getpass

• No storage of sensitive credentials

• All processing done locally

📊 Real-World Performance

During testing, the application demonstrated impressive capabilities:

• Processed 500+ emails efficiently

• Identified statements from multiple banks

• Generated analysis in ~30 seconds

• Maintained high accuracy with 80%+ matching threshold

🏗️ Project Structure

financial_analyzer/
├── main.py                 # Entry point
├── config/                 # Configuration files
├── src/                    # Core modules
│   ├── gmail_client.py     # Gmail integration
│   ├── pdf_parser.py       # PDF processing
│   ├── data_processor.py   # Analysis
│   └── visualizer.py       # Reporting
└── reports/               # Generated insights

🚀 Getting Started

1. Clone the repository

2. Install dependencies via pip install -r requirements.txt

3. Set up Gmail API credentials

4. Run python main.py

5. Follow the interactive prompts

🔮 Future Enhancements

The architecture is designed to be extensible, supporting:

• Additional bank statement formats

• More visualization types

• Enhanced analysis features

• Automated scheduling

💡 Key Learnings

Building this project taught me valuable lessons about:

• Working with Gmail's API securely

• PDF processing challenges and solutions

• Data analysis and visualization techniques

• Building user-friendly CLI applications

🎯 Impact

This tool transforms credit card statement analysis from a manual, time-consuming task into an automated, insightful process. It helps users:

• Track spending patterns effortlessly

• Monitor credit utilization

• Make informed financial decisions

• Save time on financial analysis

🔒 Privacy & Security

The application prioritizes user privacy:

• No data sent to external servers

• Secure credential handling

• Local-only processing

• Revocable API access

📈 Conclusion

This Financial Analyzer demonstrates how Python can be used to automate personal finance tasks while maintaining security and privacy. Whether you're tracking personal expenses or analyzing spending patterns, this tool provides valuable insights with minimal manual effort.

The project is open for contributions and can be extended to support more banks and features. Check out the GitHub repository to get started!

#Python #Finance #Automation #DataAnalysis #Programming