IDEAS home Printed from https://ideas.repec.org/a/rbs/ijbrss/v11y2022i7p218-225.html
   My bibliography  Save this article

The role of data analytics for detecting indications of fraud in the public sector

Author

Listed:
  • Novita Novita

    (Accounting Department, Faculty of Economics and Business, Universitas Trilogi, Jl. TMP Kalibata No. 1, Jakarta, Indonesia)

  • Anara Indrany Nanda Ayu Anissa

    (Accounting Department, Faculty of Economics and Business, Universitas Trilogi, Jl. TMP Kalibata No. 1, Jakarta, Indonesia)

Abstract

Technological developments play an important role in the audit process, one of which is the use of data analytics that are useful to assist auditors in analyzing data, collecting audit evidence, predicting risks that occur and will occur, and other things. The use of data analytics is also applied by public sector auditors to maintain accountability and responsibility for state finances. This study aims to examine the effect of using data analytics on indications of fraud for public sector examiners in Indonesia. Testing and data analysis techniques used STATA version 14, which processed answers from 33 auditors from two representative offices of public sector auditors in Java Province and Sumatra Province. The results of the study state that the use of data analytics has a positive and significant effect on indications of fraud for public sector examiners in the examination process. This means that public sector auditors can detect fraud using data analytics. Key Words:Accounting Fraud, Public Sector, Audit Quality

Suggested Citation

  • Novita Novita & Anara Indrany Nanda Ayu Anissa, 2022. "The role of data analytics for detecting indications of fraud in the public sector," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 11(7), pages 218-225, October.
    • Handle: RePEc:rbs:ijbrss:v:11:y:2022:i:7:p:218-225
    DOI: 10.20525/ijrbs.v11i7.2113
    as

    Download full text from publisher

    File URL: https://www.ssbfnet.com/ojs/index.php/ijrbs/article/view/2113/1482
    Download Restriction: no
    File URL: https://doi.org/10.20525/ijrbs.v11i7.2113
    Download Restriction: no
    File URL: https://libkey.io/10.20525/ijrbs.v11i7.2113?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Lynnette Purda & David Skillicorn, 2015. "Accounting Variables, Deception, and a Bag of Words: Assessing the Tools of Fraud Detection," Contemporary Accounting Research, John Wiley & Sons, vol. 32(3), pages 1193-1223, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jiao Ji & Oleksandr Talavera & Shuxing Yin, 2018. "The Hidden Information Content: Evidence from the Tone of Independent Director Reports," Working Papers 2018-28, Swansea University, School of Management.
    2. Muhammad Farhan Malik & Yuan George Shan & Jamie Yixing Tong, 2022. "Do auditors price litigious tone?," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 62(S1), pages 1715-1760, April.
    3. Xin Xu & Feng Xiong & Zhe An, 2023. "Using Machine Learning to Predict Corporate Fraud: Evidence Based on the GONE Framework," Journal of Business Ethics, Springer, vol. 186(1), pages 137-158, August.
    4. Zhang, Yi & Hu, Ailing & Wang, Jiahua & Zhang, Yaojie, 2022. "Detection of fraud statement based on word vector: Evidence from financial companies in China," Finance Research Letters, Elsevier, vol. 46(PB).
    5. Borchert, Philipp & Coussement, Kristof & De Weerdt, Jochen & De Caigny, Arno, 2024. "Industry-sensitive language modeling for business," European Journal of Operational Research, Elsevier, vol. 315(2), pages 691-702.
    6. Berkin, Anil & Aerts, Walter & Van Caneghem, Tom, 2023. "Feasibility analysis of machine learning for performance-related attributional statements," International Journal of Accounting Information Systems, Elsevier, vol. 48(C).
    7. Dan Amiram & Zahn Bozanic & James D. Cox & Quentin Dupont & Jonathan M. Karpoff & Richard Sloan, 2018. "Financial reporting fraud and other forms of misconduct: a multidisciplinary review of the literature," Review of Accounting Studies, Springer, vol. 23(2), pages 732-783, June.
    8. Hills, Robert & Kubic, Matthew & Mayew, William J., 2021. "State sponsors of terrorism disclosure and SEC financial reporting oversight," Journal of Accounting and Economics, Elsevier, vol. 72(1).
    9. Nerissa C. Brown & Richard M. Crowley & W. Brooke Elliott, 2020. "What Are You Saying? Using topic to Detect Financial Misreporting," Journal of Accounting Research, Wiley Blackwell, vol. 58(1), pages 237-291, March.
    10. Jun Qi & Qinwei Chi & Ni Yang & Junyan Ouyang, 2023. "The Impact of the Tone of a Prospectus on IPO Underpricing: Evidence from China," Australian Accounting Review, CPA Australia, vol. 33(4), pages 375-390, December.
    11. Abdullah Albizri & Deniz Appelbaum & Nicholas Rizzotto, 2019. "Evaluation of financial statements fraud detection research: a multi-disciplinary analysis," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 16(4), pages 206-241, December.
    12. Achakzai, Muhammad Atif Khan & Peng, Juan, 2023. "Detecting financial statement fraud using dynamic ensemble machine learning," International Review of Financial Analysis, Elsevier, vol. 89(C).
    13. Elias Zavitsanos & Dimitris Mavroeidis & Konstantinos Bougiatiotis & Eirini Spyropoulou & Lefteris Loukas & Georgios Paliouras, 2023. "Financial misstatement detection: a realistic evaluation," Papers 2305.17457, arXiv.org.
    14. Yasheng Chen & Zhuojun Wu, 2022. "Financial Fraud Detection of Listed Companies in China: A Machine Learning Approach," Sustainability, MDPI, vol. 15(1), pages 1-15, December.
    15. Dennis W. Campbell & Ruidi Shang, 2022. "Tone at the Bottom: Measuring Corporate Misconduct Risk from the Text of Employee Reviews," Management Science, INFORMS, vol. 68(9), pages 7034-7053, September.
    16. Lee, Heejae & Zhang, Lu & Liu, Qi & Vasarhelyi, Miklos, 2022. "Text Visual Analysis in Auditing: Data Analytics for Journal Entries Testing," International Journal of Accounting Information Systems, Elsevier, vol. 46(C).
    17. Shi Qiu & Yuansheng Luo & Hongwei Guo, 2021. "Multisource evidence theory‐based fraud risk assessment of China's listed companies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1524-1539, December.
    18. Zita Drábková, 2018. "Fraud Risk Management from the Perspective of CFEBT Risk Triangle of Accounting Errors and Frauds," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 66(5), pages 1261-1266.
    19. Ionela Munteanu, 2020. "Financial Reporting Quality and Operational Efficiency in the Coastal Region of Romania," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(2), pages 978-984, December.
    20. Joanna Wyrobek & Lukasz Poplawski & Marcin Surowka, 2020. "Identification of a Fraudulent Organizational Culture in Enterprises Listed in Warsaw Stock Exchange," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 2), pages 622-637.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:rbs:ijbrss:v:11:y:2022:i:7:p:218-225. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Umit Hacioglu (email available below). General contact details of provider: https://edirc.repec.org/data/ssbffea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.