@inproceedings{44530fa8ef074282ad49c3bdb49d9067,
title = "A Study of Big Data Analytics in Internal Auditing",
abstract = "As the world is progressing towards an era of automation and artificial intelligence (AI), the use of data is becoming more valuable than ever before. Many professions and organizations have already incorporated automation and AI into their work to increase their productivity and efficacy. Auditing firms are not an exception in this regard as these firms are also using many data analytics processes to plan and perform audit. This paper provides a systematic review of big data analytics application in auditing with primary focus on internal auditing. The paper contemplates the advantages of incorporating big data analytics in internal auditing. The paper further discusses the state-of-the-art and contemporary trends of big data analytics in internal auditing while also summarizing the findings of notable researches in the area. Finally, the paper outlines various challenges in incorporating big data analytics in internal auditing and provides insights into future trends.",
keywords = "Accounting, Auditing, Big data, Data analytics, External auditing, Internal auditing",
author = "Neda Shabani and Arslan Munir and Mohanty, {Saraju P.}",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; Intelligent Systems Conference, IntelliSys 2021 ; Conference date: 02-09-2021 Through 03-09-2021",
year = "2022",
doi = "10.1007/978-3-030-82196-8_27",
language = "English (US)",
isbn = "9783030821951",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "362--374",
editor = "Kohei Arai",
booktitle = "Intelligent Systems and Applications - Proceedings of the 2021 Intelligent Systems Conference IntelliSys",
address = "Germany",
}