Enhancing Online Fraud Detection: Leveraging Machine Learning and Behavioral Indicators for Improved Accuracy and Real-Time Detection
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 indi...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
|
| Series: | EPJ Web of Conferences |
| Online Access: | https://www.epj-conferences.org/articles/epjconf/pdf/2025/13/epjconf_icetsf2025_01003.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850211454400069632 |
|---|---|
| author | Shaha Prasad Gavekar Vidya |
| author_facet | Shaha Prasad Gavekar Vidya |
| author_sort | Shaha Prasad |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3d2001707c63416ca4faaa539b981afd |
| institution | OA Journals |
| issn | 2100-014X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | EPJ Web of Conferences |
| spelling | doaj-art-3d2001707c63416ca4faaa539b981afd2025-08-20T02:09:33ZengEDP SciencesEPJ Web of Conferences2100-014X2025-01-013280100310.1051/epjconf/202532801003epjconf_icetsf2025_01003Enhancing Online Fraud Detection: Leveraging Machine Learning and Behavioral Indicators for Improved Accuracy and Real-Time DetectionShaha Prasad0Gavekar Vidya1School of Management & Research, Dr. D. Y. Patil Dnyan Prasad University's School of Management & ResearchSurydatta Institute of Management and Mass CommunicationFraud 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.https://www.epj-conferences.org/articles/epjconf/pdf/2025/13/epjconf_icetsf2025_01003.pdf |
| spellingShingle | Shaha Prasad Gavekar Vidya Enhancing Online Fraud Detection: Leveraging Machine Learning and Behavioral Indicators for Improved Accuracy and Real-Time Detection EPJ Web of Conferences |
| title | Enhancing Online Fraud Detection: Leveraging Machine Learning and Behavioral Indicators for Improved Accuracy and Real-Time Detection |
| title_full | Enhancing Online Fraud Detection: Leveraging Machine Learning and Behavioral Indicators for Improved Accuracy and Real-Time Detection |
| title_fullStr | Enhancing Online Fraud Detection: Leveraging Machine Learning and Behavioral Indicators for Improved Accuracy and Real-Time Detection |
| title_full_unstemmed | Enhancing Online Fraud Detection: Leveraging Machine Learning and Behavioral Indicators for Improved Accuracy and Real-Time Detection |
| title_short | Enhancing Online Fraud Detection: Leveraging Machine Learning and Behavioral Indicators for Improved Accuracy and Real-Time Detection |
| title_sort | enhancing online fraud detection leveraging machine learning and behavioral indicators for improved accuracy and real time detection |
| url | https://www.epj-conferences.org/articles/epjconf/pdf/2025/13/epjconf_icetsf2025_01003.pdf |
| work_keys_str_mv | AT shahaprasad enhancingonlinefrauddetectionleveragingmachinelearningandbehavioralindicatorsforimprovedaccuracyandrealtimedetection AT gavekarvidya enhancingonlinefrauddetectionleveragingmachinelearningandbehavioralindicatorsforimprovedaccuracyandrealtimedetection |