Mr.V. SUDHAKAR1 , MORAMPUDI JOSHNITHA2 ,PILLI NAGA VIJAYA LAKSHMI3 , MUDDUNENI KOMALI NAGA SRI SAI KEERTIKA4 , MADDALI ROHITH BABU5 03 04 The rapid growth of online payment systems and digital transactions has significantly increased the risk of financial fraud making fraud detection an essential component of modern financial security Online fraud payment detection aims to identify suspicious or unauthorized transactions in real time to prevent financial losses for both users and financial institutions However one of the major challenges in fraud detection is the imbalance in transaction datasets where fraudulent transactions represent only a small portion compared to legitimate ones Traditional machine learning models often fail to accurately detect fraud due to this class imbalance problem This study proposes an online fraud payment detection system using balanced machine learning algorithms to improve detection accuracy and reliability The proposed approach applies data balancing techniques such as oversampling under sampling and synthetic data generation to ensure equal representation of fraudulent and nonfraudulent transactions during model training Balanced algorithms including Random Forest Support Vector Machine Logistic Regression and Gradient Boosting are utilized to enhance classification performance Feature engineering and preprocessing techniques are also employed to extract meaningful transaction patterns and reduce noise in the dataset The system evaluates performance using metrics such as accuracy precision recall F1score and ROCAUC which are more suitable for imbalanced datasets Experimental results demonstrate that balanced machine learning models significantly improve fraud detection rates while minimizing false positives The proposed framework provides a scalable and efficient solution for secure online payment environments enabling financial organizations to detect fraudulent activities proactively and enhance customer trust Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026