ABSTRACT
The main aim of this study is to test the ability of a deep neural network (DNN) to detect and to predict financial statement fraud (FSF) and non-financial statement fraud (NFSF). This study also intended to reduce increasing financial losses due to FSF and to improve the reliability of financial statements (FS) for various business and investment decision-making purposes.
A supervised DNN application is introduced in this study, accompanied by an end-to-end automated process to improve the overall efficiency in detecting and predicting FSF and NFSF, using only annual reports of public listed companies from various industries with different market capital sizes.
This study report focused on the application of DNN as the tech tool which leverages the traditional FSF-detecting techniques (financial ratios and financial risk indicators) as the basis to attempt to detect and predict the FSF. The findings of this study indicated that the proposed DNN model can predict and detect both FSF and NFSF with an accuracy score of 89.11%.
