An optimized deep neural network-based financial statement fraud detection in text mining
Identifying Financial Statement Fraud (FSF) events is very crucial in text mining. The researcher’s community is mostly utilized the data mining method for detecting FSF. In this direction, mostly the quantitative data has utilized by research i.e. the financial ratio is presented for detecting frau...
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Veröffentlicht in: | 3C empresa 2021-11, Vol.10 (4), p.77-105 |
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Sprache: | eng |
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Zusammenfassung: | Identifying Financial Statement Fraud (FSF) events is very crucial in text mining. The researcher’s
community is mostly utilized the data mining method for detecting FSF. In this direction, mostly the
quantitative data has utilized by research i.e. the financial ratio is presented for detecting fraud in
financial statements. On the text investigation there is no researches like auditor's remarks present in
published reports. For this reason, this paper develops the optimized deep neural network-based FSF
detection in the qualitative data present in financial reports. The pre-processing of text is performed
initially using filtering, lemmatization, and tokenization. Then, the feature selection is done by the
Harris Hawks Optimization (HHO) algorithm. Finally, a Deep Neural Network-Based Deer Hunting
Optimization (DNN-DHO) is utilized to identify the fraud or no-fraud report in the financial statements.
The developed FSF detection methodology executed in Python environment using financial statement
datasets. The output of the developed approach gives high classification accuracy (96%) in comparison
to the standard classifiers like DNN, CART, LR, SVM, Bayes, BP-NN, and KNN. Also, it provides better
outcomes in all performance metrics. |
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ISSN: | 2254-3376 2254-3376 |
DOI: | 10.17993/3cemp.2021.100448.77-105 |