IDEAS home Printed from https://ideas.repec.org/a/eee/bushor/v58y2015i5p493-500.html
   My bibliography  Save this article

Data analytics in auditing: Opportunities and challenges

Author

Listed:
  • Earley, Christine E.

Abstract

In this article, I provide background regarding a hot topic in the public accounting profession: the rise of big data and the related field of data analytics (DA). The tax and advisory practices of public accounting firms have embraced the use of DA, and firms have made significant investments in growing these practice areas. Although DA holds great promise for the auditing practice as well, the use of widespread DA on audit engagements has lagged behind other practice areas. This is due to the fact that auditing presents unique challenges in the adoption of DA that are not relevant for other practice areas. Despite the impression that DA is not being embraced as readily in auditing, public accounting firms are continuing to make significant investments in developing audit-related DA, and it is only a matter of time before we start to see the transformational impact of these efforts. The purpose of this article is (1) to explain how DA applies to financial statement audits and why it could represent a game changer in how audits are conducted, and (2) to provide a context for researchers in terms of problems to be addressed related to DA.

Suggested Citation

  • Earley, Christine E., 2015. "Data analytics in auditing: Opportunities and challenges," Business Horizons, Elsevier, vol. 58(5), pages 493-500.
    • Handle: RePEc:eee:bushor:v:58:y:2015:i:5:p:493-500
    DOI: 10.1016/j.bushor.2015.05.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0007681315000592
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.bushor.2015.05.002?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gray, Glen L. & Debreceny, Roger S., 2014. "A taxonomy to guide research on the application of data mining to fraud detection in financial statement audits," International Journal of Accounting Information Systems, Elsevier, vol. 15(4), pages 357-380.
    2. Crawley, Michael & Wahlen, James, 2014. "Analytics in empirical/archival financial accounting research," Business Horizons, Elsevier, vol. 57(5), pages 583-593.
    3. Mary B. Curtis & Elizabeth A. Payne, 2014. "Modeling voluntary CAAT utilization decisions in auditing," Managerial Auditing Journal, Emerald Group Publishing, vol. 29(4), pages 304-326, April.
    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. Federica De Santis, 2018. "Big Data e revisione contabile: uno studio esplorativo nel contesto italiano," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2018(2), pages 129-154.
    2. Ali, Abdul & Mancha, Ruben & Pachamanova, Dessislava, 2018. "Correcting analytics maturity myopia," Business Horizons, Elsevier, vol. 61(2), pages 211-219.
    3. Vicky Arnold, 2018. "The changing technological environment and the future of behavioural research in accounting," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(2), pages 315-339, June.
    4. Freiman, Jamie W. & Kim, Yongbum & Vasarhelyi, Miklos A., 2022. "Full population testing: Applying multidimensional audit data sampling (MADS) to general ledger data auditing," International Journal of Accounting Information Systems, Elsevier, vol. 46(C).
    5. Dilla, William N. & Raschke, Robyn L., 2015. "Data visualization for fraud detection: Practice implications and a call for future research," International Journal of Accounting Information Systems, Elsevier, vol. 16(C), pages 1-22.
    6. Maestrini, Vieri & Luzzini, Davide & Caniato, Federico & Ronchi, Stefano, 2018. "Effects of monitoring and incentives on supplier performance: An agency theory perspective," International Journal of Production Economics, Elsevier, vol. 203(C), pages 322-332.
    7. Siew, Eu-Gene & Rosli, Khairina & Yeow, Paul H.P., 2020. "Organizational and environmental influences in the adoption of computer-assisted audit tools and techniques (CAATTs) by audit firms in Malaysia," International Journal of Accounting Information Systems, Elsevier, vol. 36(C).
    8. Mirjana Pejić Bach & Živko Krstić & Sanja Seljan & Lejla Turulja, 2019. "Text Mining for Big Data Analysis in Financial Sector: A Literature Review," Sustainability, MDPI, vol. 11(5), pages 1-27, February.
    9. Ruhnke, Klaus, 2023. "Empirical research frameworks in a changing world: The case of audit data analytics," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 51(C).
    10. Shan, Yuan George & Troshani, Indrit & Tarca, Ann, 2019. "Managerial ownership, audit firm size, and audit fees: Australian evidence," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 35(C), pages 18-36.
    11. Mushang Lee & Yu-Lan Huang, 2020. "Corporate Social Responsibility and Corporate Performance: A Hybrid Text Mining Algorithm," Sustainability, MDPI, vol. 12(8), pages 1-19, April.
    12. 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.
    13. Gianluca Gabrielli & Alice Medioli & Paolo Andrei, 2022. "Accounting and Big Data: Trends, opportunities and direction for practitioners and researchers," FINANCIAL REPORTING, FrancoAngeli Editore, vol. 2022(2), pages 89-112.
    14. Ahmad Almagrashi & Abdulwahab Mujalli & Tehmina Khan & Osama Attia, 2023. "Factors determining internal auditors’ behavioral intention to use computer-assisted auditing techniques: an extension of the UTAUT model and an empirical study," Future Business Journal, Springer, vol. 9(1), pages 1-19, December.
    15. Alles, Michael & Gray, Glen L., 2016. "Incorporating big data in audits: Identifying inhibitors and a research agenda to address those inhibitors," International Journal of Accounting Information Systems, Elsevier, vol. 22(C), pages 44-59.
    16. Jianfei Shen & Lincong Han, 2020. "RETRACTED ARTICLE: Design process optimization and profit calculation module development simulation analysis of financial accounting information system based on particle swarm optimization (PSO)," Information Systems and e-Business Management, Springer, vol. 18(4), pages 809-822, December.
    17. Fábio Albuquerque & Paula Gomes Dos Santos, 2023. "Recent Trends in Accounting and Information System Research: A Literature Review Using Textual Analysis Tools," FinTech, MDPI, vol. 2(2), pages 1-27, April.
    18. Chen, Yuh-Jen & Liou, Wan-Ching & Chen, Yuh-Min & Wu, Jyun-Han, 2019. "Fraud detection for financial statements of business groups," International Journal of Accounting Information Systems, Elsevier, vol. 32(C), pages 1-23.
    19. Yuan Song & Hongwei Wang & Maoran Zhu, 2018. "Sustainable strategy for corporate governance based on the sentiment analysis of financial reports with CSR," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 4(1), pages 1-14, December.
    20. Laskai András, 2019. "AI foundations of the international business planning and the AI consciousness model," International Journal of Science and Business, IJSAB International, vol. 3(1), pages 17-28.

    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:eee:bushor:v:58:y:2015:i:5:p:493-500. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/bushor .

    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.