Global Strategies in Fraud Prevention and Detection: A Systematic Review

This study used a qualitative descriptive design of a systematic review of Scopus-indexed journals on fraud detection and prevention from 2021-2025, and the result was 39 relevant articles. The results highlight that fraud prevention and detection both require a harmonized approach through internal auditing, internal controls, technology solutions, and organizational factors like leadership and professional skepticism. The use of sophisticated technologies such as machine learning, deep learning, and big data analytics significantly enhances detection capabilities, especially in the case of financial transactions. Besides internal control systems, auditor skills, and whistleblower systems, there are also significant roles to be played. Ethical concerns, such as privacy and transparency issues within AI-driven systems, have been noticed as well. Managerial implications consist of keeping organizational internal controls robust, utilizing technological tools, and encouraging a culture of skepticism as well as ethical governance. Future research may focus on the long-term effectiveness of these methods, such as ethical considerations in AI, sectoral applications, and the cost-effectiveness of implementing these solutions in resource-constrained environments.