Embracing Textual Data Analytics in Auditing with Deep Learning

• Ting Sun ^[1] ; Miklos A. Vasarhelyi ^[1]
  1. [1] Rutgers Business School. NJ, USA
• Localización: The International Journal of Digital Accounting Research, ISSN 1577-8517, Vol. 18, Nº. 24, 2018, págs. 49-67
• Idioma: inglés
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• Resumen

  • While the massive volume of text documents from multiple sources inside and outside of the company provides more information for auditors, the lack of efficient and effective technology solutions hampers the full use of text data. Powered by the emerging data analytics technology of deep learning, the value of the text can be better explored to deliver a higher quality of audit evidence and more relevant business insights. This research analyzes the usefulness of the information provided by various textual data in auditing and introduces deep learning, an evolving Artificial Intelligence approach. Furthermore, it provides a guide for auditors to implement deep learning techniques with pre-developed tools and open-source libraries.