Fraud Detection and Prevention in E-Commerce: Machine Learning Approaches to Secure Transactions

E-commerce has grown as a leading marketplace for world trade, but this market has been contaminated by new risks like fraud. Some of the past techniques of detecting fraud do not do well in handling new methods of fraud in the market. The purpose of this research is to understand the offers of the ML approaches for increasing the efficiency of the fraud detection and prevention in e-Commerce processer. This study shows the utility of using different types of the ML solution alone and in comparison, to one another for real-time cyber fraud identification by demonstrating how and why their false positives and overall effectiveness are lower than other methods, as well as how the models adapt to new tactics. Issues like quality of data used, bias in either selection or process of the algorithm are also covered.