Identifying Credit Card Fraud with Machine Learning: Evaluation of Algorithms and Oversampling Techniques

Abstract

The physical loss of a credit card or the theft of sensitive credit card data is referred to as credit card fraud. For detection, a variety of machine learning methods can be applied. This study presents several algorithms for identifying transactions as authentic or fraudulent. The study used the credit card fraud detection dataset. The SMOTE approach was utilized for oversampling due to the extremely unbalanced nature of the dataset. Additionally, a feature selection process was carried out, and the dataset was divided into training and test sets. The experiment employed eight machine learning models, including Random Forest, AdaBoost, Support Vector Classifier, Extreme Gradient Boost, and Logistic Regression algorithms. The findings indicate that few algorithms have a high degree of accuracy when detecting credit card fraud. The Random Forest model can be applied to find further anomalies. This approach has proven to be effective in accurately detecting fraudulent activity and reducing the number of false positives and negatives. The results of the experiment also highlight the importance of feature selection in improving the performance of the models by the highest accuracy score with 96%, precision of 100, Recall of 91, and F1Scroe of 95. By using a combination of different machine learning algorithms, financial institutions can enhance their fraud detection systems and better protect their customers from potential financial losses. The findings from this study demonstrate the potential for significant advancements in the field of fraud detection using machine learning techniques.