Credit Card Fraud Detection Using Machine Learning Algorithms

Abstract

The increasing use of credit cards in various transactions has resulted in an upsurge in fraudulent activities. This has caused significant financial losses for both individuals and businesses. This research attempted to focus on developing an efficient credit card fraud detection system using machine learning algorithms. Specifically, the Random Forest, Logistic Regression, K-nearest neighbours, Decision Trees, and naive Bayes algorithms were used to analyze the dataset and predict fraudulent activities. The dataset was preprocessed, and feature engineering techniques were applied to improve the performance of the models. Experimental results show that the Random Forest algorithm outperformed other models with an accuracy rate of 99.95%, precision of 0.85%, and recall of 0.85%. These findings indicate the potential of using machine learning algorithms in detecting credit card fraud, and the proposed system could be implemented in financial institutions and payment processing companies to improve their fraud detection systems.