Effective fraud detection in e-commerce: Leveraging machine learning and big data analytics

Abstract

Sophisticated cyber-infrastructure and information technology methods are necessary to exploit and analyse the massive amounts of data generated by online transactions. This study introduces a big data platform for online retailers to tackle various issues in the e-commerce industry. Both people and businesses are vulnerable to fraud, which is a worldwide problem. In today's tech-driven society, the battle against fraud has been greatly aided by machine learning (ML) and artificial intelligence (AI). This essay takes a look at the conventional wisdom about fraud prevention and shows how outdated it is when compared to modern fraud techniques. It delves further into the ways in which ML and AI are supporting fast digitization, which in turn revolutionises fraud prevention efforts. Machine learning and artificial intelligence algorithms enable companies to comb through massive amounts of data for patterns and anomalies that could suggest fraudulent activity. In this article, we will explore how machine learning and artificial intelligence may greatly enhance fraud prevention efforts. These technologies can help with advanced data analytics, anomaly detection, and predictive modelling. The text highlights the ways in which these technologies empower organisations to proactively identify and reduce fraud risks, protecting both their operations and stakeholders.

Keywords

• Fraud detection
• E-commerce
• Machine learning
• Big data
• Online transaction