A Data Mining Approach To Detection Financial Distress In Iraqi Companies - Dalya Abdulkarim Abdullah, Nashaat Jasim AL-Anber (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Due to the difficulties experienced by the financial auditors and the management analyst, in order to know the financial performance of the company and the ability of companies to continue and because of the inconsistency of the financial information being not transparent so renewed the direction of accounting work to use artificial intelligence methods and data mining techniques. In this paper, data Mining (DM) and deep learning (DL) methods were used to detect financial distress, using Artificial Neural Networks (ANN) algorithm represented by the Multilayer Perception Feed Forward Neural Network Error Back Propagation Algorithm (MLP-FFNN) as well as the C4.5 algorithm and the Multi-class support vector machine (MSVM).The results of the analysis showed that the C4.5, ANN and MSVM algorithm had the highest rate of rating accuracy by a small margin on all scales and were (97.98 , 96.97 , 91.92) respectively. In this study, the data of companies listed on the Iraq stock exchange for 2017 were taken, including 36 companies with high financial distress, 20 with medium financial distress and 43 non-distressed for a group of 99 companies .