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Financial fraud detection and big data analytics – implications on auditors’ use of fraud brainstorming session

Financial fraud detection and big data analytics – implications on auditors’ use of fraud... PurposeThis paper aims to discuss the application of Big Data analytics to the brainstorming session in the current auditing standards.Design/methodology/approachThe authors review the literature related to fraud, brainstorming sessions and Big Data, and propose a model that auditors can follow during the brainstorming sessions by applying Big Data analytics at different steps.FindingsThe existing audit practice aimed at identifying the fraud risk factors needs enhancement, due to the inefficient use of unstructured data. The brainstorming session provides a useful setting for such concern as it draws on collective wisdom and encourages idea generation. The integration of Big Data analytics into brainstorming can broaden the information size, strengthen the results from analytical procedures and facilitate auditors’ communication. In the model proposed, an audit team can use Big Data tools at every step of the brainstorming process, including initial data collection, data integration, fraud indicator identification, group meetings, conclusions and documentation.Originality/valueThe proposed model can both address the current issues contained in brainstorming (e.g. low-quality discussions and production blocking) and improve the overall effectiveness of fraud detection. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Managerial Auditing Journal Emerald Publishing

Financial fraud detection and big data analytics – implications on auditors’ use of fraud brainstorming session

Managerial Auditing Journal , Volume 34 (3): 14 – Mar 4, 2019

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
0268-6902
DOI
10.1108/MAJ-01-2018-1767
Publisher site
See Article on Publisher Site

Abstract

PurposeThis paper aims to discuss the application of Big Data analytics to the brainstorming session in the current auditing standards.Design/methodology/approachThe authors review the literature related to fraud, brainstorming sessions and Big Data, and propose a model that auditors can follow during the brainstorming sessions by applying Big Data analytics at different steps.FindingsThe existing audit practice aimed at identifying the fraud risk factors needs enhancement, due to the inefficient use of unstructured data. The brainstorming session provides a useful setting for such concern as it draws on collective wisdom and encourages idea generation. The integration of Big Data analytics into brainstorming can broaden the information size, strengthen the results from analytical procedures and facilitate auditors’ communication. In the model proposed, an audit team can use Big Data tools at every step of the brainstorming process, including initial data collection, data integration, fraud indicator identification, group meetings, conclusions and documentation.Originality/valueThe proposed model can both address the current issues contained in brainstorming (e.g. low-quality discussions and production blocking) and improve the overall effectiveness of fraud detection.

Journal

Managerial Auditing JournalEmerald Publishing

Published: Mar 4, 2019

References

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