Enhancing Credit Card Fraud Detection with LSTM and Random Forest: A Practical Approach

Credit card fraud detection is an ongoing challenge in the digital age. The sheer volume of transactions makes it difficult to identify fraudulent activity in real-time without sophisticated tools.

In this guide, we’ll explore a practical and efficient approach that leverages Long Short-Term Memory (LSTM) networks for feature extraction and Random Forest for classification.

This method balances computational efficiency with performance, making it suitable for those with limited resources who still require robust fraud detection.

Why This Approach?

You might wonder why we chose LSTM and Random Forest for this task. Here’s why:

1. LSTM for Feature Extraction:

• Sequential Data Handling: LSTMs are excellent at capturing temporal dependencies in sequential data. Although the credit card data isn’t time series in the traditional sense, LSTM can help reduce feature space while retaining important patterns.
• Dimensionality Reduction: High-dimensional datasets can be challenging for traditional machine learning models. The LSTM autoencoder compresses the data, making it more manageable and less prone to overfitting.