Optimizing Credit Card Fraud Detection with Random Forests and SMOTE

Credit card fraud is a growing concern in the financial industry, necessitating the development of efficient detection methods to minimize financial losses. In this project, we built a machine learning-based system to detect fraudulent transactions using a publicly available dataset of credit card transactions. The dataset, highly imbalanced with fraudulent transactions representing less than 0.2% of the total, was processed using techniques like Synthetic Minority Over-sampling Technique (SMOTE) to handle class imbalance. Various classification algorithms, including Logistic Regression, Random Forest, and Neural Networks, were evaluated to determine their effectiveness in identifying fraudulent activities. The Random Forest model emerged as the best performing algorithm with an accuracy of 99.5% and a high recall score, indicating its robustness in detecting fraudulent transactions. This system can be deployed in real-time financial systems to enhance fraud prevention mechanisms and ensure secure financial transactions.