Analyzing Credit Card Fraud Cases with Supervised Machine Learning Methods: Logistic Regression and Naive Bayes

Abstract

Frauds involving credit cards are simple and simple to target. With the rise of online payment credit cards have had a huge role in our daily life and economy for the past two decades and it is an important task for companies to identify fraud and non-fraud transactions. As the number of credit cards grows every day and the volume of transactions increases quickly in tandem, fraudsters who wish to exploit this market for illegitimate gains have come to light. Nowadays, it's quite simple to access anyone's credit card information, which makes it simpler for card fraudsters to do their crimes. Thanks to advances in technology, it is now possible to determine whether information gained with malicious intent has been used by looking at the costs and time involved in altering account transactions. The Credit Card Fraud analysis data set, which was obtained from the Kaggle database, was used in the modeling process together with The Logistic regression method and Naive Bayes algorithms. Using the Knime platform, we are going to apply machine learning techniques to practical data in this study. The goal of this study is to identify who performed the transaction by examining the periods when people used their credit cards. The Logistic regression approach and the Naive Bayes method both had success rates of 99.83%, which was the highest. The two methods' results are based on Cohen's kappa, accuracy, precision, recall, and other metrics. These and many more outcomes are shown in the confusion matrix.