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Mining corporate annual reports for intelligent detection of financial statement fraud - A comparative study of machine learning methods

机译：挖掘公司年度报告以智能检测财务报表欺诈-机器学习方法的比较研究

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Financial statement fraud has been serious concern for investors, audit firms, government regulators, and other capital market stakeholders. Intelligent financial statement fraud detection systems have therefore been developed to support decision-making of the stakeholders. Fraudulent misrepresentation of financial statements in managerial comments has been noticed in recent studies. As such, the purpose of this study was to examine whether an improved financial fraud detection system could be developed by combining specific features derived from financial information and managerial comments in corporate annual reports. To develop this system, we employed both intelligent feature selection and classification using a wide range of machine learning methods. We found that ensemble methods outperformed the remaining methods in terms of true positive rate (fraudulent firms correctly classified as fraudulent). In contrast, Bayesian belief networks (BBN) performed best on non-fraudulent firms (true negative rate). This finding is important because interpretable "green flag" values (for which fraud is likely absent) could be derived, providing potential decision support to auditors during client selection or audit planning. We also observe that both financial statements and text in annual reports can be utilised to detect non-fraudulent firms. However, non-annual report data (analysts' forecasts of revenues and earnings) are necessary to detect fraudulent firms. This finding has important implications for selecting variables when developing early warning systems of financial statement fraud. (C) 2017 Elsevier B.V. All rights reserved.
机译：财务报表舞弊一直是投资者，审计公司，政府监管机构和其他资本市场利益相关者的严重关切。因此，已经开发了智能的财务报表欺诈检测系统来支持利益相关者的决策。在最近的研究中已经注意到在管理评论中舞弊的虚假陈述。因此，本研究的目的是检验是否可以通过结合从公司年度报告中的财务信息和管理评论中得出的特定功能来开发改进的财务欺诈检测系统。为了开发该系统，我们使用了广泛的机器学习方法来进行智能特征选择和分类。我们发现，在真阳性率方面（整体上被正确分类为欺诈的欺诈性公司），集成方法优于其他方法。相比之下，贝叶斯信念网络（BBN）在非欺诈性公司中的表现最佳（真实否定率）。这一发现很重要，因为可以得出可解释的“绿色标记”值（可能不存在欺诈行为），从而在客户选择或审计计划期间为审计师提供潜在的决策支持。我们还注意到，财务报表和年度报告中的文字都可以用来检测不欺诈的公司。但是，非年度报告数据（分析师对收入和收益的预测）对于检测欺诈性公司是必需的。这一发现对开发财务报表欺诈预警系统时选择变量具有重要意义。 （C）2017 Elsevier B.V.保留所有权利。

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