A Systematic Review on Machine Learning-based Fraud Detection System in E-Commerce
Ankush Kumar Shah, Parminder Singh · 2024
The use of the internet practically everywhere in the world has become a new trend as digitalization advances. The majority of users are making day-to-day purchases of goods using e-commerce platforms such as Flipkart, Amazon, Myntra, etc., but there will be certain dangers associated with payment. Due to the fact that we consider it secure while making payments and have a decreased likelihood of spotting fraud, we are able to develop transaction processing systems, such as Internet-based banking, in a safe manner. However, there are significant hurdles that come along with learning about and having access to the advantages and benefits that are provided by E-Commerce systems. Security is one of the main problems that we can encounter presently. Internet traffic has been increasing daily because people are spending more time on these online platforms. One of the most important elements and difficulties of online transactions is fraud detection. Online fraud is now on the rise, along with the number of transactions, and it is even more difficult to identify the fraud transactions. By utilising some approaches, such as Information Fusion Technology (IFT), Big Data Mining (BDM), Logistic Regression, Support Vector Machine (SVM), Random Forest, etc., we can stop fraudulent actions in E-Commerce Systems. Fraudulent behaviours may be analysed and found with great accuracy by machine learning. So the main goal of this paper is to offer an examination of e-commerce system security.