Real-Time Fraud Detection in E-Commerce Using Machine Learning Models

The number of transactions is increasing since more and more individuals are buying things online. Our research also shows that online transactional misrepresentation is on the increase. There will soon be a meteoric rise in the application of machine learning for the purpose of detecting and preventing online fraud.  Inventions always come with their fair share of troubles, and the web-based system during COVID-19 was no exception; there were just too many people utilizing it and making too many transactions online. An e-commerce business can expand in several ways. Online stores rely on fraud detection and prevention systems to be operational. Even while ML plays a significant role in these anti-fraud activities, the organizational context in which these ML models operate must be considered. This study takes an organization-centric approach to the problem of fraud detection by constructing an operational model of anti-fraud departments in e-commerce enterprises.