Comparative Analysis of Machine Learning Algorithms on Credit Card Fraud Detection

Fraud detection is a crucial area not only for credit cards but also for industries handling financial transactions where fraud detection is crucial. Machine Learning (ML) had come up powerfully with a variety of tools that can process large amounts of data and can find complex hidden patterns from the data. This abstract provides the usage of ML Algorithms in Fraud Detection with different types of techniques used. In this paper a comparison has been made between different algorithms based on their Accuracy, Precision, Recall, and F1-score. Algorithms Used are SVM, KNN, Decision Tree, Random Forest, Logistic Regression. Dataset was collected from Kaggle and it contains 284806 rows and a total of 31 columns.