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Text Visual Analysis in Auditing: Data Analytics for Journal Entries Testing

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  • Lee, Heejae
  • Zhang, Lu
  • Liu, Qi
  • Vasarhelyi, Miklos

Abstract

Business transaction data includes numeric values of the transactions and the date/time when the transactions are recorded, and textual data such as descriptions. Understanding the textual information of business transactions is also important since this information captures the nature of transactions in a qualitative manner. This study proposes a text visual analysis approach for auditing. We argue that combining text analysis and data visualization can improve the efficiency of audit data analytics for textual data in the organization's accounting information system. We provide a demonstration of the proposed method using a year-around general ledger data set. We use data visualization software Orange and Tableau for the demonstration. The proposed method can be used to understand a client's business and identify abnormal or unusual transactions from not only quantitative information but also qualitative information.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:ijoais:v:46:y:2022:i:c:s1467089522000239
    DOI: 10.1016/j.accinf.2022.100571
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    References listed on IDEAS

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    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.
    2. Gelman A. & Pasarica C. & Dodhia R., 2002. "Lets Practice What We Preach: Turning Tables into Graphs," The American Statistician, American Statistical Association, vol. 56, pages 121-130, May.
    3. Moriarity, S, 1979. "Communicating Financial Information Through Multidimensional Graphics," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 17(1), pages 205-224.
    4. Hoelscher, Jamie & Mortimer, Amanda, 2018. "Using Tableau to visualize data and drive decision-making," Journal of Accounting Education, Elsevier, vol. 44(C), pages 49-59.
    5. Lisa Maria Perkhofer & Peter Hofer & Conny Walchshofer & Thomas Plank & Hans-Christian Jetter, 2019. "Interactive visualization of big data in the field of accounting," Journal of Applied Accounting Research, Emerald Group Publishing Limited, vol. 20(4), pages 497-525, June.
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    1. Anca Pianoschi & Stefania Mierlita, 2025. "Revising ISA240 in a digital world: the sociomaterial perspective on fraud, technology, and stakeholder influence," Digital Finance, Springer, vol. 7(4), pages 921-947, December.

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