Utilizing Data Analytics To Identify Fraud Potential: An Internal Auditor's Perspective - Anggraini, Leriza Desitama; Saluza, Imelda; Faradillah , Faradillah (Profesional Muda Cendekia Publishing, 2025)
Research Aims: This research aims to analyze the influence of Data Analytics on the detection of potential fraud. Design/methodology/approach: The methodology used is Structural Equation Modeling-Partial Least Squares (SEM-PLS) with data collected through questionnaires distributed to respondents, namely internal auditors who work in South Sumatra Province and Bangka Belitung Province. Research Findings: The research results show that Descriptive Analytics and Diagnostic Analytics have a significant influence on fraud detection and prevention, while Predictive Analytics and Prescriptive Analytics do not show a significant influence. These findings indicate that internal auditors are more effective in using descriptive and diagnostic analytics in fraud detection efforts, while the application of predictive and prescriptive analytics is still limited. Theoretical Contribution/Originality: The theoretical contribution of this research is to enrich the literature regarding the role of data analytics in fraud prevention, especially in the context of internal auditors in Indonesia. This research also provides insight into the importance of developing the capacity of internal auditors in using data analytics to increase the effectiveness of fraud monitoring and detection systems in organizations. Keywords: Data Analytics, Internal Auditor, Fraud