Abstract
Purpose:This study aims to examine the use of intelligent automation in the process of detecting and investigating fraud. Methodology/approach:This research is a bibliometric-based systematic literature review (SLR) related to fraud detection. The research sample consisted of 75 articles obtained from the Science Direct, Emerald Insight, IEE, and MDPI databases for the period 2020–2023. Findings: The results of the research show that machine learning and deep learning are the most popular fraud detection techniques used by researchers, and the field of credit card fraud is the most popular field used as a research object. The fields of property insurance, health, cyber phishing, taxation, Shell companies, social programs, Ponzi schemes, and supply chain management are the ones that have the least amount of research, namely only one article for each of these fields. Practical implications: The result show that there are smart tools in detecting fraud in several fields, but it has not been explained whether the existence of these tools can reduce fraud. Originality/value: This research provides novelty in the use of intelligent automation in the process of detecting and investigating fraud.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call