E-Commerce Site Payment Fraud Detection using Machine Learning Technique
T. Baldwin Immanuel, Manikandan K, Muthukumar P, A. Arikesh · 2025
Online Fraudulent are unauthorized users and accessing data of a user without asking permission from appropriate. This activity relates to threat of data and several other security measures based vulnerable functions. Increase in growth and usage of E-commerce site led to usage of credit and debit cards to pay bills. Recently fraudulent activist prevention in the field of online payment using bank details is a difficult task. Finding of unauthorised usage in paying bill is identified manually with the available data of recent transaction. This process is accurate but it consumes more time and tedious process. Many researchers focus on this problem and to ensure the safety of users by enabling protocol measures by avoiding the intrusion fraudulent. This study brings an emerging solution by utilizing data mining technique. The machine learning (ML) used to developing and training model is plenty in use. Among that this paper utilizes K-nearest neighbor (KNN) classifier to identify unauthorised usage and identify with face recognition technique. The proposed model is trained with the image of authorised user and makes the user to allow and deny usage of his credit/debit card by unauthorised users. The performance analysis is done with accuracy, precision and f1 score value. This system gains accuracy of 98%.