Credit Card Fraud Detection based on Random Forest Model

Credit card fraud detection is a critical challenge in the financial sector, necessitating effective models for handling imbalanced data. This study explores the application of the Random Forest algorithm, emphasizing its performance in addressing the intricacies of data distribution. Building upon existing research, the model is refined through advanced data processing methods and optimized hyperparameters. The proposed solution encompasses detailed insights into data processing, tackles imbalanced data challenges using Synthetic Minority Over-sampling Technique (SMOTE), and employs Random Forest with entropy as the guiding criterion. Evaluation metrics, including accuracy, precision, recall, and F1-Score, assess model performance. Comparative results showcase the model's efficacy in reducing undetected fraudulent transactions, particularly in scenarios with imbalanced data. The discussion delves into the nuanced trade-offs between false positives and false negatives, highlighting the model's adaptability in diverse data distributions. The study concludes by outlining future research directions, emphasizing scalability, personalized detection capabilities, and real-world testing to enhance credit card fraud detection systems.