Fraudulent financial reporting and data analytics: an explanatory study from Ireland

Ahmed Aboud, Barry Robinson

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose - This paper aims to explore the effectiveness of fraud prevention and detection techniques, including data analytics, machine learning and data mining, and to understand how widespread the use of data analytics is across different sectors and to identify and understand the potential barriers to implementing these techniques to detect and prevent fraud.

Design/methodology/approach - A survey was administered to 73 Irish businesses to determine to what extent traditional approach, data mining or text mining are being used to prevent or detect fraudulent financial reporting, and to determine the perception level of their effectiveness.

Findings
- The study suggests that whilst data analytics is widely used by businesses in Ireland there is an under-utilisation of data analytics as an effective tool in the fight against fraud. The study suggests there are barriers that may be preventing companies from implementing advanced data analytics to detect financial statement fraud and identifies how those barriers may be overcome.

Originality/value
- In contrast to the majority of literature on big data analytics and auditing, which lacks empirical insight into the diffusion, effectiveness and obstacles of data analytics, this explanatory study contributes by providing useful insights from the field on big data analytics. While the extant auditing literature generally addresses the avenues of big data utilisation in auditing domain, our study explores particularly the use big data analytics as a fraud prevention and detection techniques.
Original languageEnglish
JournalAccounting Research Journal
Early online date4 Nov 2020
DOIs
Publication statusEarly online - 4 Nov 2020

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