Factors affecting fraud detection: Evidence from Indonesia’s supreme audit institutions
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DOIhttp://dx.doi.org/10.21511/pmf.15(1).2026.05
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Article InfoVolume 15 2026, Issue #1, pp. 58-67
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Type of the article: Research Article
Abstract
Improving fraud prevention processes requires systematic and ongoing efforts as part of implementing accountability, transparency, and integrity. By strengthening legislation and enhancing expertise, the government, audit companies, and financial management organizations must work together to establish an environment that lowers the likelihood of fraud. The goal of this study is to ascertain how internal audit, workload, and internal control affect auditors’ capacity to identify fraud. Seventy government auditors from Indonesia’s Supreme Audit Institution’s Principal Inspectorate, who had been employed for at least 2 years, were given Google Forms surveys to collect data for this study. Partial least squares (SmartPLS) with a significance level of 5% was used for the analysis. The results showed that internal audit (β = 0.419; p < 0.05) and internal control (β = 0.325; p < 0.05) had a beneficial effect on fraud detection. However, workload had no effect (β = 0.255; p > 0.05). The audit body can increase risk-based audit techniques by using the research findings about factors impacting fraud detection, which will enable auditors to concentrate more on areas with high fraud potential. In order to enable faster and more accurate fraud detection, the financial and development audit agency may also include these findings when developing technical audit standards based on information technology and data analytics. Consequently, this study directly aids the financial and development audit agency in improving the effectiveness and efficiency of the audit process in detecting fraud.
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JEL Classification (Paper profile tab)J83, K42, M41, M42
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References50
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Tables7
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Figures0
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- Table 1. Measurement of research variables
- Table 2. List of Indonesian supreme audit agencies
- Table 3. Response rate
- Table 4. Description of the respondents
- Table 5. Descriptive statistics
- Table 6. Convergent validity and reliability
- Table 7. PLS path algorithm and bootstrapping
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