aspg-3-3376-1791417145 20261007235225 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2025 17 2 Aditi Sharma Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India S. Phani Praveen Department of CSE, PVP Siddhartha Institute of Technology, Vijayawada, A.P, India Vipin Tiwari Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India Pradeep Kumar Arya School of Computer Science Engineering and Technology, Bennett University, Grater Noida, UP, 201310, India Deepak Parvathaneni Naga Srinivasu Department of CSE, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amaravati 522503, Andhra Pradesh, India Mukta Patel Department of CSE, Parul Institute of Technology, Parul University, Vadodara, Gujarat, India An inventive deep learning-based method for identifying financial fraud, revolutionizing e-commerce security in the process. The research offers a state-of-the-art setup that makes use of deep learning computations in the dynamic world of online exchanges, where the possibility of fraudulent activity is a danger. Since frauds are known to be erratic and lack consistency, it might be challenging to spot them. Con artists exploit the latest developments in technology. They manage to evade security measures, which results in millions of dollars being lost. One method of tracking fraudulent exchanges is to use information-mining techniques to investigate and detect unusual behaviours. Interactions. In contrast to deep learning techniques as auto encoders, convolutional neural networks (CNN), restricted Boltzmann machines (RBM), and deep belief networks (DBN), this paper aims to benchmark several machine-learning techniques, such as k-nearest neighbour (KNN), irregular forest, and support vector machines (SVM). The three-evaluation metrics that are really employed are the Area Under the ROC Curve (AUC), the Matthews Correlation Coefficient (MCC), and the Cost of Failure. 2025 366 376 3376 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.170227 https://www.americaspg.com/journal/3/article/3376 https://www.americaspg.com/storage/21733675190.pdf