International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, Issue 1, pp. 15-20, January 2026.
This work is licensed under a Creative Commons Attribution 4.0 International License.
M.A.Manivasagam
K Nagesh
P Hemalatha
M Mounika
V Hanumanthu
P Kamesh
Department of CSE, Siddartha Institute of Science and Technology, Puttur, India.
Abstract: As the rapid development of digital financial services has accelerated, transaction integrity has become an exigent priority. Traditional rule-based fraud detection mechanisms are now ineffective against the continuously rising sophistication of fraud strategies. This paper proposes an integrated platform for enhanced transaction safety through machine learning and AI-driven automated fraud detection systems. The study integrates supervised and unsupervised learning paradigms to identify anomaly patterns in transactions for timely prevention and detection of fraud. The model uses feature engineering and real-time analytics to improve accuracy, scalability, and flexibility across diverse financial environments. The comparative analysis with legacy systems indicates substantial improvements in detection accuracy, reduced false positives, and faster response times. The research highlights that adaptive algorithms and learning mechanisms are essential to stimulate resilience to emerging threats with ongoing learning. It provides a firm foundation for financial organizations and electronic-commerce websites to encourage user confidence, provide regulation, and obtain safe transactions within the digital economy.
Keywords: Fraud Detection, Transaction Security, Machine Learning, Artificial Intelligence, Anomaly Detection, Financial Technology.
References:
- F. M. Ahamadabad, P. A. Dastjerdi, and M. Nasseri, “Spatiotemporal GRACE TWS downscaling using statistical and machine learning methods: Random Forest, area-to-area kriging, and hybrid methods,” Journal of Hydrology Regional Studies, vol. 62, p. 102885, Oct. 2025, doi: 10.1016/j.ejrh.2025.102885.
- K. H. Ahmed, S. Axelsson, Y. Li, and A. M. Sagheer, “A credit card fraud detection approach based on ensemble machine learning classifier with hybrid data sampling,” Machine Learning With Applications, vol. 20, p. 100675, May 2025, doi: 10.1016/j.mlwa.2025.100675.
- W. Sliti and O. Besbes, “Drone-guard: A self-supervised deep learning framework for real-time spatiotemporal anomaly detection in UAV surveillance systems,” Neurocomputing, vol. 653, p. 131168, Aug. 2025, doi: 10.1016/j.neucom.2025.131168.
- R. K. Gupta et al., “Enhanced framework for credit card fraud detection using robust feature selection and a stacking ensemble model approach,” Results in Engineering, vol. 26, p. 105084, Apr. 2025, doi: 10.1016/j.rineng.2025.105084.
- O. O. Tooki and O. M. Popoola, “A systematic review on blockchain-based energy trading in a decentralized transactive energy system: Opportunities, complexities, strategic challenges, research directions,” Results in Engineering, vol. 27, p. 106237, Jul. 2025, doi: 10.1016/j.rineng.2025.106237.
- J. Qian and G. Tong, “Metapath-guided graph neural networks for financial fraud detection,” Computers & Electrical Engineering, vol. 126, p. 110428, May 2025, doi: 10.1016/j.compeleceng.2025.110428.
- N. Jayakrishna and N. N. Prasanth, “Detection and mitigation of distributed denial of service attacks in vehicular ad hoc network using a spatiotemporal deep learning and reinforcement learning approach,” Results in Engineering, vol. 26, p. 104839, Apr. 2025, doi: 10.1016/j.rineng.2025.104839.
- N. Uddin, “Role of AI in Preventing Financial Crime: A Comprehensive Analytical review,” Journal of Economic Criminology, p. 100200, Oct. 2025, doi: 10.1016/j.jeconc.2025.100200.
- M. Al Rafi et al., “CCFD-SSL: Optimizing Real-Time Credit Card Fraud Detection Using Self-Supervised Learning and Contrastive Representations,” 2024 IEEE 3rd International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things (RAAICON), Dhaka, Bangladesh, 2024, pp. 258-263, doi: 10.1109/RAAICON64172.2024.10928582.
- M. Andronie et al., “Generative artificial intelligence algorithms in Internet of Things blockchain-based fintech management,” Oeconomia Copernicana, vol. 15, no. 4, pp. 1349–1381, Dec. 2024, doi: 10.24136/oc.3283.
- M. M. Hameed, R. Ahmad, L. M. Kiah, and G. Murtaza, “Machine learning-based offline signature verification systems: A systematic review,” Signal Processing Image Communication, vol. 93, p. 116139, Jan. 2021, doi: 10.1016/j.image.2021.116139.