A corpus driven computational intelligence framework for deception detection in financial text
Financial fraud rampages onwards seemingly uncontained. The annual cost of fraud in the UK is estimated to be as high as ÂŁ193bn a year [1] . From a data science perspective and hitherto less explored this thesis demonstrates how the use of linguistic features to drive data mining algorithms can aid in unravelling fraud. To this end, the spotlight is turned on Financial Statement Fraud (FSF), known to be the costliest type of fraud [2]. A new corpus of 6.3 million words is composed of102 annual reports/10-K (narrative sections) from firms formally indicted for FSF juxtaposed with 306 non-fraud firms of similar size and industrial grouping. Differently from other similar studies, this thesis uniquely takes a wide angled view and extracts a range of features of different categories from the corpus. These linguistic correlates of deception are uncovered using a variety of techniques and tools. Corpus linguistics methodology is applied to extract keywords and to examine linguistic structure. N-grams are extracted to draw out collocations. Readability measurement in financial text is advanced through the extraction of new indices that probe the text at a deeper level. Cognitive and perceptual processes are also
Minhas, Saliha Z.
Machine Learning ; Financial Statement Fraud ; Classififcation ; Clustering ; Language ; Readability ; Corpus Linguistics ; Financial Fraud ; Deception Detection ; Unstructured Text ; Language and computers--Data processing ; Corpora (Linguistics)--Data processing ; Misleading financial statements ; Fraud
A corpus driven computational intelligence framework for deception detection in financial text. Financial fraud rampages onwards seemingly uncontained. The annual cost of fraud in the UK is estimated to be as high as ÂŁ193bn a year [1] . From a data science perspective and hitherto less explored this thesis demonstrates how the use of linguistic features to drive data mining algorithms can aid in unravelling fraud. To this end, the spotlight is turned on Financial Statement Fraud (FSF), known to be the costliest type of fraud [2]. A new corpus of 6.3 million words is composed of102 annual reports/10-K (narrative sections) from firms formally indicted for FSF juxtaposed with 306 non-fraud firms of similar size and industrial grouping. Differently from other similar studies, this thesis uniquely takes a wide angled view and extracts a range of features of different categories from the corpus. These linguistic correlates of deception are uncovered using a variety of techniques and tools. Corpus linguistics methodology is applied to extract keywords and to examine linguistic structure. N-grams are extracted to draw out collocations. Readability measurement in financial text is advanced through the extraction of new indices that probe the text at a deeper level. Cognitive and perceptual processes are also
corpus drive computational intelligence framework deception detection financial text financial onwards seemingly annual cost estimate high data science perspective hitherto less_explored demonstrates use linguistic_feature drive data mining algorithm aid unravelling end spotlight turn financial statement know costly type new corpus word compose annual reports narrative section firm formally juxtapose non firm similar size industrial grouping differently similar study uniquely take_wide view extract range feature different_category corpus linguistic correlate deception uncover use_variety technique tool corpus_linguistics apply extract keywords examine linguistic_structure extract draw collocation readability measurement financial text advance extraction new index probe text deep level cognitive perceptual process also