Credit Card Fraud Detection Using Machine Learning

Abstract

The fraud done to credit cards has become a critical issue affecting financial institutions and consumers globally, leading to significant financial losses. This paper presents machine learning approach is employed to identify fraudulent credit card transactions by analyzing transaction patterns and behaviors. Several supervised learning algorithms, such as Random Forest, Sup-port Vector Machine, and Decision Tree, are assessed for their ability to detect fraudulent activities effectively. The study ad-dresses challenges such as data imbalance and evolving fraud tactics through feature engineering and model optimization. Experimental results demonstrate that ensemble methods pro-vide superior accuracy and reduce false positive rates, enhancing the reliability of fraud detection systems. The proposed approach contributes to strengthening financial security and protecting users from unauthorized transactions.