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

Heejae Lee, Lu Zhang, Qi Liu and Miklos Vasarhelyi

International Journal of Accounting Information Systems, 2022, vol. 46, issue C

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.

Keywords: Audit data analytics; Text mining; Journal entry testing; Risk assessment (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ijoais:v:46:y:2022:i:c:s1467089522000239

DOI: 10.1016/j.accinf.2022.100571

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