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From Spin to Swindle: Identifying Falsification in Financial Text.
Cognitive Computation ( IF 5.4 ) Pub Date : 2016-05-21 , DOI: 10.1007/s12559-016-9413-9
Saliha Minhas 1 , Amir Hussain 1
Affiliation  

Despite legislative attempts to curtail financial statement fraud, it continues unabated. This study makes a renewed attempt to aid in detecting this misconduct using linguistic analysis with data mining on narrative sections of annual reports/10-K form. Different from the features used in similar research, this paper extracts three distinct sets of features from a newly constructed corpus of narratives (408 annual reports/10-K, 6.5 million words) from fraud and non-fraud firms. Separately each of these three sets of features is put through a suite of classification algorithms, to determine classifier performance in this binary fraud/non-fraud discrimination task. From the results produced, there is a clear indication that the language deployed by management engaged in wilful falsification of firm performance is discernibly different from truth-tellers. For the first time, this new interdisciplinary research extracts features for readability at a much deeper level, attempts to draw out collocations using n-grams and measures tone using appropriate financial dictionaries. This linguistic analysis with machine learning-driven data mining approach to fraud detection could be used by auditors in assessing financial reporting of firms and early detection of possible misdemeanours.

中文翻译:

从旋转到欺诈:在财务文本中识别伪造。

尽管立法机构试图减少财务报表舞弊,但舞弊仍在继续。这项研究进行了新的尝试,以使用语言分析和年度报告/ 10-K形式的叙事部分上的数据挖掘来帮助检测这种不当行为。与类似研究中使用的功能不同,本文从欺诈和非欺诈性公司的新建叙事语料库(408份年度报告/ 10-K,650万个单词)中提取了三组不同的特征。这三套功能中的每套功能分别通过一套分类算法来确定此二进制欺诈/非欺诈歧视任务中的分类器性能。从产生的结果来看,有明确的迹象表明,蓄意篡改公司绩效的管理层使用的语言与讲真话的人明显不同。这项新的跨学科研究首次在更深层次上提取了可读性功能,并尝试使用使用适当的财务词典来测量n克和音调。借助机器学习驱动的数据挖掘方法进行的语言分析,以进行欺诈检测,审计人员可以将其用于评估公司的财务报告和及早发现可能的轻罪。
更新日期:2016-05-21
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