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Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: From Spin to Swindle: Identifying Falsification in Financial Text
Author(s): Minhas, Saliha
Hussain, Amir
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Keywords: Classification
Financial statement fraud
Issue Date: Aug-2016
Date Deposited: 3-Jun-2016
Citation: Minhas S & Hussain A (2016) From Spin to Swindle: Identifying Falsification in Financial Text. Cognitive Computation, 8 (4), pp. 729-745.
Abstract: 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 usingn-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.
DOI Link: 10.1007/s12559-016-9413-9
Rights: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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