Smart Ways to Spot Fake Credit Card Tricks

The Problem

• Credit cards are ubiquitous, used for both online and in-store transactions.
• Fraud remains a significant issue, though rare compared to legitimate transactions.
• Challenge: How to efficiently detect fraudulent activities?

The Solution: Feature Selection

• Computers can assist in fraud detection, but they need to be trained.
• Feature selection helps identify the most relevant data points.
• Three key methods:
  1. Pearson correlation – Removes redundant data.
  2. Information gain – Identifies useful information.
  3. Random forest importance – Highlights critical features.

Testing the Approach

• Tested on five datasets using five machine learning models:
• Random Forest
• Extra Trees
• XGBoost
• AdaBoost
• CatBoost
• Results: The new method outperformed older techniques.

Real-World Applications

• Works on real-world data, not just simulations.
• Goal: Real-time fraud detection to protect businesses.

Future Considerations

• Is this the best possible method? More research may be needed.
• For now, it’s a promising step in fraud prevention.