Performance Analysis Algorithm Classification and Regression Trees and Naive Bayes Based Particle Swarm Optimization for Credit Card Transaction Fraud Detection

Abstract

With the advancement of technology, credit cards have become a popular tool for transactions, both physically and online, due to their ease of use and seamless integration with banking systems. However, with the increasing use of credit cards, the cases of fraud have also risen, resulting in financial losses for both cardholders and banks. To address this issue, effective and efficient credit card transaction fraud detection has become a top priority. Using machine learning algorithms is one of the techniques that can be employed to detect fraud in credit card transactions. The purpose of this research is to determine the performance and find the best method of the CART algorithm, Naive Bayes, and their combination with Particle Swarm Optimization (PSO) in detecting fraud in credit card transaction histories. The data used consists of 568,630 big data entries with parameters including id, V1-V28, amount, and class. The research results obtained are as follows: the accuracy of the Naive Bayes algorithm is 93.15%, precision is 94%, recall is 93%, and AUC is 0.99. For the CART algorithm, the accuracy is 99.96%, with precision and recall at 100%, and AUC at 1.00. Additionally, the Naive Bayes algorithm combined with PSO achieved an accuracy of 98.50%, precision and recall of 98%, and AUC of 1.00. Lastly, the CART algorithm combined with PSO reached an accuracy of 99.97%, with precision and recall at 100%, and AUC at 1.00. It can be concluded that the best method resulting from the tests conducted is the Classification and Regression Trees method combined with Particle Swarm Optimization.