Evaluating the impact of risk management and risk-based internal audit on fraud detection in local governments

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One of the leading indicators for improving fraud detection capability is the evaluation of the implementation of risk management and risk-based internal audit and information technology systems. This study aims to evaluate how risk management and risk-based internal audits influence fraud detection capabilities in local government when integrated with information technology systems. SPSS 26 was utilized for data analysis. The paper uses a quantitative approach, collecting primary data with questionnaires distributed among 200 auditors and 70 supervisors across four districts in West Java. A purposive sampling approach based on self-selection was used. The findings show that the ability to detect fraud is significantly and positively influenced by the implementation of a risk-based internal audit (sig 0.000 < 0.05) and risk management process (sig 0.000 < 0.05). On the other hand, the risk management framework (sig 0.107 > 0.05) has a negative and insignificant effect on improving fraud detection capability. In addition, the relationships between the risk management process (sig 0.006 < 0.05) and fraud detection capability were found to be moderated by information technology systems. However, information technology systems are unable to moderate the relationship between risk-based internal audit (sig 0.563 > 0.05) and risk management framework (sig 0.115 > 0.05) on fraud detection capability. Therefore, risk management and risk-based internal audits are able to detect fraud, and information technology systems can strengthen the risk management process with the ability to detect fraud.

Acknowledgment
The authors would like to thank the University of North Sumatra, especially the Research Institute, for its support and the Ministry of Education and Research through the Directorate of Research, Technology, and Community Service program for providing intellectual assistance and funding for this project in the PMDSU grant (number: 91/UN5.4.10.S/PPM/KP-DRTPM/2024).

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    • Figure 1. Conceptual framework
    • Table 1. Descriptive statistics
    • Table 2. Normality test
    • Table 3. Multicollinearity test
    • Table 4. Heteroscedasticity test
    • Table 5. Hypotheses results
    • Table 6. Coefficient of determination
    • Table 7. Moderation test results
    • Table 8. Coefficient of determination test results
    • Conceptualization
      Angginun Juwita Sari Harahap, Erlina
    • Data curation
      Angginun Juwita Sari Harahap, Erlina
    • Formal Analysis
      Angginun Juwita Sari Harahap, Erlina
    • Funding acquisition
      Angginun Juwita Sari Harahap, Erlina
    • Investigation
      Angginun Juwita Sari Harahap, Erlina
    • Project administration
      Angginun Juwita Sari Harahap, Erlina
    • Resources
      Angginun Juwita Sari Harahap, Erlina
    • Software
      Angginun Juwita Sari Harahap, Erlina
    • Supervision
      Angginun Juwita Sari Harahap, Erlina
    • Validation
      Angginun Juwita Sari Harahap, Erlina
    • Writing – original draft
      Angginun Juwita Sari Harahap, Erlina
    • Writing – review & editing
      Angginun Juwita Sari Harahap, Erlina
    • Methodology
      Erlina