## 5 Conclusions  
This study proposed a better FSF detection model by utilizing data from publicly available financial statements of firms in the MENA region. An FSF prediction model was developed using the ensemble technique and the XGBoost algorithm while investigating the utilization of various ML techniques in detecting financial statement fraud using published financial disclosures. Upsampling was performed on the dataset using the SMOTE technique to prevent class imbalance issues. In our experiments, SMOTE proved to be a beneficial metric for sampling data with a large class imbalance. When learning on an unbalanced dataset, this study shows that XGBoost outperforms other techniques for the task of FSF detection. We conducted analysis and comparison of three individual ML classifiers and three ensemble techniques used widely in FSF detection using a dataset comprising companies from the MENA region. We used SVM, DT, and LR as individual classifiers and RF, AdaBoost, and XGBoost as ensemble techniques. While all the classifier models yielded an acceptable accuracy rate, the simulation results from Table 4 indicate that the XGBoost classifier is the most efficient model for financial statement fraud detection when using these settings. In the next phase of the study, the XGBoost classifier was further optimized by hyperparameter tuning with cross-validation to obtain the best model \begin{table}
\begin{tabular}{l l} \hline \hline
**Steps** & **Description** \\ \hline Hyperparameter tuning using & ‘learning_rate’: [0.03, 0.01, 0.003, 0.001], \\ RandomizedSearchCV on accuracy scores with & ‘min_child_weight’: [1, 3, 5, 7, 10], \\ n\_iter = 1000 and three-fold cross-validation & ‘gamma’: [0, 0.5, 1, 1.5, 2, 2.5, 5], \\  & ‘subsample’: [0.6, 0.8, 1.0, 1.2, 1.4], \\  & ‘colsample_bytree’: [0, 6.0, 0.8, 1.0, 1.2, 1.4], \\  & ‘max\_depth’: [3, 4, 5, 6, 7, 8, 9, 10, 12, 14], \\  & ‘reg\_lambda’:[0.4, 0.6, 0.8, 1, 1.2, 1.4] \\ \hline Model training until validation_0- error & Fitting achieved by three-fold cross-validation \\ improvement in ten rounds & for each of 1000 candidates, totalling 3000 folds. \\  & The best iteration for each round is the one \\  & with the least validation error. \\ \hline Select parameters & ‘learning_rate’: 0.03, \\  & ‘min\_child\_weight’: 3, \\  & ‘gamma’: 1.5, \\  & ‘subsample’: 0.8, \\  & ‘colsample\_bytree’: 1.0, \\  & ‘max\_depth’: 9, \\  & ‘reg\_lambda’: 1 \\ \hline \hline Obtain the best accuracy score & 0.9605, a significant improvement on the \\  & accuracy score of 0.9366 in the previous stage. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: XGBoost optimization process. for the problem. The simulation results indicated that the proposed model has higher performance compared to both classic ML models and ensemble models. In this study, we have considered only financial attributes. Analysis of the decentralized model and of non-financial attributes may be incorporated in the future. All authors contributed together to realize this work, and their exact contributions are difficult to specify. Conceptualization and methodology by A.A.A., S.K. and A.M.K. : Software and original draft preparation by A.A.A. and [PERSON]-B.; Validation, writing, reviewing, editing, and supervision by M.E.-B., S.K. and A.M.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Available on request. The authors declare that they have no competing interests.