Improving Online E-Commerce Fraud Detection Using Deep Learning Autoencoder Models: A Research Approach

In order to guarantee the security of transactions and preserve consumer confidence in the ubiquitous domain of online e-commerce, it is necessary to implement improved detection mechanisms. The objective of this project is to examine the potential for enhancing fraud detection systems on e-commerce platforms by employing deep learning autoencoder models. Autoencoders are particularly well-suited for the identification of fraudulent activity because of their exceptional capacity to identify anomalies in extensive datasets and their proficiency in unsupervised learning. This study introduces a viable approach to distinguishing between legitimate and fraudulent transactions by utilising the reconstruction error measure that is inherent to autoencoders. On real-world e-commerce datasets, experimental evaluations have shown that the proposed model outperforms current machine learning methods in terms of precision, recall, and detection accuracy. The research results illuminate the potential of deep learning autoencoders for real-time fraud detection systems, emphasising their significance in the prevention of online purchasing fraud.