The Influence of Big Data, CAATs, and Auditor Religiosity on Fraud Detection with Task-Specific Knowledge as A Moderating Variable

Abstract

Fraud remains a persistent threat to financial governance, requiring auditors to integrate technological tools and professional expertise to enhance fraud detection capabilities. This study examined the effects of Big Data, Computer-Assisted Audit Techniques (CAATs), and auditor religiosity on fraud detection while assessing task-specific knowledge (TSK) as a moderating variable. A quantitative causal-associative research design was employed using primary data collected through an online five-point Likert-scale questionnaire from Indonesian auditors selected through purposive and convenience sampling. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4, including measurement model evaluation, coefficient of determination assessment, effect-size analysis, and hypothesis testing.The findings showed that CAATs had a significant positive effect on fraud detection (? = 0.260; p = 0.044), whereas Big Data (? = 0.039; p = 0.417) and auditor religiosity (? = 0.005; p = 0.487) did not have significant effects. TSK significantly predicted fraud detection directly (? = 0.601; p = 0.001) but did not moderate the effects of Big Data, CAATs, or auditor religiosity. The model explained 66.7% of the adjusted variance in fraud detection. Therefore, audit organizations should prioritize CAATs adoption and strengthen auditors’ task-specific knowledge while progressively developing Big Data capabilities and related technical competencies to improve fraud detection effectiveness.