https://doi.org/10.1051/epjconf/202532801003
Enhancing Online Fraud Detection: Leveraging Machine Learning and Behavioral Indicators for Improved Accuracy and Real-Time Detection
1 School of Management & Research, Dr. D. Y. Patil Dnyan Prasad University's School of Management & Research, Pune, Maharashtra, India
2 Surydatta Institute of Management and Mass Communication, Pune, Maharashtra, India
* Corresponding author: shahaprasad@gmail.com
Published online: 18 June 2025
Fraud detection remains a critical challenge in financial security, requiring robust and efficient methodologies to identify fraudulent transactions accurately. This study presents a comprehensive evaluation of machine learning (ML) models for fraud detection, emphasizing the role of behavioral indicators in enhancing model performance. A comparative analysis of traditional and advanced ML models, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), Artificial Neural Networks (ANN), and LightGBM, was conducted using real-world fraud detection datasets. LightGBM, the proposed model, outperformed other methods, achieving the highest ROC-AUC (0.981), F1-score (0.902), and lowest false positive rate (0.006). The study also highlights the importance of feature selection, class imbalance handling, and real-world applicability by discussing computational efficiency and deployment challenges. These findings contribute to the growing body of fraud detection research by offering a practical, scalable, and high-accuracy ML approach for real-time fraud prevention systems.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
