This Python script analyzes a batch of financial transaction data to detect potentially fraudulent transactions based on predefined rules. It uses Pandas to process the data and identifies high-value transactions and location anomalies.

Check it out on Github.

Features

  • Loads transaction data from a CSV file.
  • Detects high-value transactions based on a fixed threshold.
  • Identifies location anomalies based on user’s historical location data.
  • Generates a report summarizing the detected anomalies.
  • Produces alert messages for suspicious activity.

Usage

  1. Place your transaction data in a CSV file named transactions.csv.
  2. Run the script: python fraud_detection.py

Future Considerations and Improvements

While this script provides a basic framework for fraud detection, it can be significantly improved to reflect real-world scenarios:

  • Dynamic Transaction Thresholds:
    • The current fixed threshold for high-value transactions does not account for variations in user spending patterns or economic contexts. In a real-world system, thresholds should be dynamic and user-specific.
    • Consider calculating thresholds based on:
      • User’s income or average spending.
      • Location-specific economic indicators.
      • Transaction frequency.
  • Currency Conversion:
    • If dealing with international transactions, currency conversion is essential. A fixed threshold across different currencies is not meaningful.
  • Card-Based Location Anomalies:
    • Users may have multiple cards with different spending patterns or location usage. Location anomalies should be tracked per card rather than per user.
  • Expanded Fraud Rules:
    • Implement more sophisticated rules, such as:
      • Time-based anomalies (e.g., unusual transaction times).
      • Frequent transactions within a short period.
      • Velocity checks (sudden increase in transaction volume).
  • Machine Learning:
    • Integrate machine learning models to detect more complex fraud patterns.
  • Real-time Alerts:
    • Implement real-time alerting via email, SMS, or a dashboard.
  • Database Integration:
    • Store transaction data in a database for efficient querying and analysis.
  • Logging:
    • Add robust logging for audit trails and debugging.