SMART FRAUD DETECTION SYSTEM FOR E-COMMERCE AND ONLINE TRANSACTION USING MACHINE-LEARNING

The rapid expansion of e-commerce platforms and online payment systems has led to a significant increase in digital transactions, accompanied by a rise in fraudulent activities.Detecting fraud in real time has become a critical challenge due to the large volume, high velocity, and complex nature of transaction data.Traditional rule-based fraud detection systems often fail to identify evolving fraud patterns and result in high false positive rates.To overcome these limitations, this project proposes a Smart Fraud Detection System for E-Commerce and Online Transactions using Machine Learning.This system utilizes machine learning techniques to analyze historical transaction data and identify hidden patterns associated with legitimate customer behavior.The Apriori algorithm is employed to detect fake transactions.Transactions that significantly deviate from established behavioral rules are flagged as potentially fraudulent.Product-related fraud detection in real time using NAIVE BAYES ALGORITHM (supervised) The integration of machine learning-driven pattern recognition with Apriori-based association rule mining enhances detection accuracy while reducing false alarms.This system improves transaction security, minimizes financial losses, and strengthens customer trust.The proposed solution is scalable, efficient, and suitable for real-time deployment in modern e-commerce platforms, contributing to a safer and more reliable digital transaction environment.