Credit Card Fraud Detection Using Random Forest Algorithm

Introduction

Credit card fraud detection is a critical area in financial security, aiming to identify fraudulent transactions among millions of legitimate ones. This project proposal outlines a system that leverages the Random Forest algorithm to improve the accuracy and efficiency of fraud detection in real-time.

Background

Recent research has demonstrated that machine learning algorithms, particularly ensemble methods like Random Forest, significantly enhance the performance of fraud detection systems. These systems analyze transaction patterns and identify anomalies that may indicate fraudulent activity. The Random Forest algorithm is particularly effective due to its ability to handle large datasets and complex interactions between features.

Project Objective

The primary objective of this project is to develop a robust credit card fraud detection system using the Random Forest algorithm. This system aims to improve upon existing methods by incorporating advanced feature selection techniques and leveraging large-scale transaction datasets.

Methodology

1. Data Collection and Preprocessing

• Datasets: Utilize publicly available datasets such as the Kaggle Credit Card Fraud Detection dataset for training and evaluation.
• Feature Engineering: Extract relevant features such as transaction amount, time, location, and merchant details, and perform data normalization and balancing to handle class imbalance.

2. Model Development

• Random Forest Algorithm: Implement the Random Forest algorithm, which involves building multiple decision trees and aggregating their predictions to improve accuracy and reduce overfitting.
• Hyperparameter Tuning: Optimize parameters such as the number of trees, maximum depth, and minimum samples per leaf to enhance model performance.

3. Training and Evaluation

• Training: Use cross-validation techniques to train the model on a balanced dataset.
• Evaluation Metrics: Measure performance using metrics such as accuracy, precision, recall, F1-score, and area under the ROC curve (AUC-ROC).

Expected Outcomes

The proposed system is expected to achieve higher accuracy in detecting fraudulent transactions compared to traditional rule-based systems. By utilizing the Random Forest algorithm, the system should effectively handle large volumes of data and adapt to evolving fraud patterns.

Conclusion

This project aims to advance the field of credit card fraud detection by developing a state-of-the-art system capable of accurately identifying fraudulent transactions in real-time. The integration of advanced machine learning techniques is anticipated to provide significant improvements in performance.

For further details on related research, please refer to the paper "Real-time Credit Card Fraud Detection Using Machine Learning," available at https://ieeexplore.ieee.org/document/8776942.