2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 17 , Issue 2 , PP: 366-376, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Aditi Sharma 1 , S. Phani Praveen 2 , Vipin Tiwari 3 , Pradeep Kumar Arya 4 * , Deepak Parvathaneni Naga Srinivasu 5 , Mukta Patel 6
Doi: https://doi.org/10.54216/FPA.170227
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.
Novel Approach , Financial , Fraud Detection , Deep Learning , E-Commerce , Machine Learning
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