Fraud Detection in Credit Card Transactional Data Using Hybrid Machine Learning Algorithm
Bhukya Dharma, Dandu Madhavi Latha · 2025
Now-a-days credit cards have grown significantly because of quick development of e-commerce and online banking. This leads to many fraud transactions. Normally, credit card frauds occur mainly when the card is lost at an unauthorized purpose or any fraudsters attempt to utilize card as well as the person using card for his/her online payment transparency. This credit card fraud detection can be solved by using Machine Learning (ML)techniques, when required data is gathered and available. This analysis presents hybrid ML approach for fraud detection in credit card transactions. The dataset used in this analysis is collected 284,807 transactions in September 2013 from European cardholders. For developing hybrid model ML techniques like Support Vector Machine (SVM) and Logistic Regression (LR) are used. The results of Hybrid ML methods are depended on Accuracy, Precision, Recall, and F1-score. Results states that, described model achieves high performance parameters Accuracy, Precision, Recall and F1-score of 97%,96%,97%,970/0 respectively than other models.