V2I6P48

Harnessing Technology for Effective Fraud Detection: Tools, Trends, and Case Studies

Taiwo Justice Olorunlana^1*

Abstract

Fraud is one of the major problems facing world economies today, with the speed of digitization, consequently rapidly the scope and sophistication of fraud keep increasing. This article reviews the advanced technological applications being used in detecting and preventing fraud in sectors, including finance, healthcare, e-commerce and government. The key technologies reviewed are machine learning, artificial intelligence, blockchain, big data analytics and biometrics, and how each applies to real-time fraud detection and prevention is analyzed. Through a comparative analysis of traditional versus tech-based methods, the article aims to highlight the constraints with manual methods, demonstrating how automated solutions instantly offer improved speeds, scalability and accuracy as fraud detection systems. The case studies look at implementations in banking, retail and healthcare while discussing contentious issues including data privacy, false positives and integration complexity. Finally, the article discusses the recommended paths forward, such as predictive AI, federated learning and autonomous detection systems, which would in turn offer strategic pathways for businesses, governments and consumers towards building a secure digital ecosystem.

Keywords:

Fraud Detection, Artificial Intelligence (AI), Machine Learning, Big Data Analytics, Blockchain, Cybersecurity, Biometric Authentication, Reg-Tech, Predictive Modeling, Internet of Things (IoT)