Prediction of Financial Statement Fraud using Machine Learning Techniques in UAE

Abstract

The purpose of this study is to predict the potential occurrence of financial statement fraud in the United Arab Emirates (UAE) companies using machine learning (ML) techniques in Python, including Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), and Neural Network (NN) techniques. The data was collected from the UAE Securities and Commodities Authority. The Bloomberg and Osiris databases were also used to collect data for UAE companies, excluding financial firms, for the period from 2010 to 2018. ML techniques in Python were used to predict the potential of financial statement fraud in UAE companies. The results show that SVM, with 89.54% accuracy and a 77.18% F1 score, outperforms all other classifiers, including LR, DT and NN. The findings of the study are significant to businesses that wish to determine the importance of prediction of financial statement fraud using ML techniques. This research project is different from other existing studies because it is conducted within the context of the UAE.