FRAUD DETECTION AUTOMATION THROUGH DATA ANALYTICS AND ARTIFICIAL INTELLIGENCE
Abstract
This study aims to review the use of data analytics and artificial intelligence in fraud detection to support internal audits. This study employs a qualitative method with a scoping review approach. The research data comprised 24 online journal articles indexed by Scopus and Sinta, which were used as the basis for scoping reviews. The stages carried out in this study consisted of identifying research questions, using keywords, selecting literature, mapping the results of research data, and compiling a summary of research results. This study concludes that the fraud detection model based on data analytics and artificial intelligence has a high accuracy value in improving audit quality. This study indicates that the Indonesian Financial and Development Supervisory Agency needs to increase the use of technology, including data analytics and artificial intelligence, to detect fraud optimally.
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