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A Survey of Automated Financial Statement Fraud Detection with Relevance to the South African Context

机译：关于南非语境相关性的自动财务报表欺诈检测调查

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Financial statement fraud has been on the increase in the past two decades and includes prominent scandals such as Enron, WorldCom and more recently in South Africa, Steinhoff. These scandals have led to billions of dollars being lost in the form of market capitalisation from different stock exchanges across the world. During this time, there has been an increase in the literature on applying automated methods to detecting financial statement fraud using publicly available data. This paper provides a survey of the literature on automated financial statement fraud detection and identifies current gaps in the literature. The paper highlights a number of important considerations in the implementation of financial statement fraud detection decision support systems, including 1) the definition of fraud, 2) features used for detecting fraud, 3) region of the case study, dataset size and imbalance, 4) algorithms used for detection, 5) approach to feature selection / feature engineering, 6) treatment of missing data, and 7) performance measure used. The current study discusses how these and other implementation factors could be approached within the South African context.
机译：财务报表欺诈在过去二十年中一直在增加，包括突出的丑闻，如安龙，世界各地，最近在南非，Steinhoff。这些丑闻导致数十亿美元以世界各地的不同股票交流的市场资本化形式丢失。在此期间，在应用自动化方法以使用公共数据的数据来检测财务报表欺诈的文献有所增加。本文对自动化财务报表欺诈检测的文献提供了一项调查，并确定了文献中的当前差距。本文突出了在实施财务报表欺诈检测决策支持系统的情况下的一些重要考虑因素，包括1）欺诈定义，2）用于检测欺诈的特征，3）案例研究区域，数据集大小和不平衡，4用于检测的算法，5）采用特征选择/特征工程的方法，6）处理缺失数据，7）使用的性能测量。目前的研究讨论了如何在南非背景下接近这些和其他实施因素。

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