Online Fraud Detection using Machine Learning
Diksha Dhiman, Amita Bisht, Anita Kumari, Dr Harishchander Anandaram, Shaurydeep Saxena, Kapil Joshi
Abstract
Fraudsters find it easy to commit credit card fraud because it is an easy target. There has been an increase in online payment modes in due to e-commerce and other online platforms, there is now a higher danger of online fraud. Due to an increase in fraudulent online transactions, researchers have begun to evaluate and detect fraud using machine learning. In order to examine past transaction information and extract consumer behavioral patterns, our main goal in this study, a novel fraud detection algorithm for streaming transaction data is built and created. A system that clusters cardholders according to the amount of their transactions. In order to extract the behavioral pattern of the groups, we should aggregate the sliding window method transactions done by cards from various groupings. It is then decided which classifier with the best rating score can be chosen as one of the best methods to predict frauds after training different classifiers over the groups separately. The paper shows how the model is related to convolutional neural networks and afterward adding classifiers algorithms, for example, Isolation Forest, Local Outlier, and SVM can be utilized to recognize misrepresentation. As a result, concept drift can be solved via a feedback mechanism. We used the Kaggle credit card fraud dataset for this article.