An optimized deep neural network-based financial statement fraud detection in text mining

Abstract

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.