Article
CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING
The exponential growth of online transactions has led to a significant rise in credit card fraud, causing substantial financial losses to financial institutions and customers. Detecting fraudulent transactions is a challenging task due to the high volume of transaction data and the evolving nature of fraud patterns. This paper presents a machine learning-based approach for credit card fraud detection using the Random Forest algorithm. The proposed system analyzes historical transaction data and classifies transactions as genuine or fraudulent. Various preprocessing techniques such as data cleaning, normalization, and feature extraction are applied to enhance model performance. The system is evaluated using performance metrics such as accuracy, precision, recall, and F1-score. Experimental results demonstrate that the Random Forest algorithm provides superior performance compared to traditional methods, achieving high accuracy and robustness in fraud detection.
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