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.

中文翻译：

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欺诈性财务报告和数据分析：来自爱尔兰的一项解释性研究

目的

本文旨在探讨欺诈预防和检测技术（包括数据分析、机器学习和数据挖掘）的有效性，并了解数据分析在不同部门的广泛应用，并识别和了解实施这些技术的潜在障碍检测和防止欺诈。

设计/方法/方法

对 73 家爱尔兰企业进行了一项调查，以确定传统方法、数据挖掘或文本挖掘在多大程度上被用于防止或检测欺诈性财务报告，并确定其有效性的感知水平。

发现

该研究表明，虽然数据分析被爱尔兰的企业广泛使用，但数据分析作为打击欺诈的有效工具并未得到充分利用。该研究表明，可能存在阻碍公司实施高级数据分析以检测财务报表欺诈并确定如何克服这些障碍的障碍。

原创性/价值

与大多数关于大数据分析和审计的文献缺乏对数据分析的传播、有效性和障碍的经验洞察力相比，这项解释性研究通过提供大数据分析领域的有用见解而做出贡献。虽然现有的审计文献通常涉及审计领域大数据利用的途径，但我们的研究特别探讨了使用大数据分析作为欺诈预防和检测技术。