Artificial Intelligence Applications in Fraud Detection and Prevention: Emerging Opportunities

Abstract

In today's digital landscape, technology plays a central role in nearly every aspect of business, including supply chain management, manufacturing, sales, marketing, and finance. However, the increasing reliance on digitisation has made organisations across various sectors more vulnerable to fraud. As businesses adopt technology to enhance efficiency, their exposure to these risks grows, necessitating the protection of intellectual property, business data, consumer information, and more. Recently, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as promising tools for detecting and preventing fraud. This article explores the potential of AI and ML to collaborate with both supervised and unsupervised systems to better address security risks. By analysing financial transactions, customer behaviour, and real-time traffic, these technologies can detect anomalies and raise alerts for suspected fraud. This study investigates the fraud detection and prevention capabilities of AI applications in the e-commerce, healthcare, and tourism sectors. Data is collected and analysed to provide meaningful insights into the managerial factors influencing various AI applications in fraud detection and prevention. The analysis of different AI applications and software, focusing on their technological models, key features, and industry use, demonstrates that tech developers have successfully integrated fraud monitoring and detection systems. Furthermore, these applications could be adapted for use in other sectors to address critical security infrastructure gaps. The survey results also strongly indicate that while organisational strategy, structure, resources, and trust support the implementation of AI, broader environmental factors such as organisational culture may significantly affect the effectiveness of AI in fraud detection and prevention.