## 2 Related Research  
Financial Statement Fraud (FSF) detection has been under the limelight for the past few decades, with emphasis placed on accounting anomalies broadly and on financial statement fraud specifically. While initial research made use of statistical or traditional techniques that are both time-consuming and expensive, more recently the focus has drifted with the emergence of big data and ML [19]. The statistical approaches are centered on traditional mathematical methods, while methods involving ML are focused on learning and intelligence. While both categories have many similarities, the key difference between them is that statistical methods are more rigid, while ML methods are able to learn from and adapt to the problem domain [20]. In addition, previous studies have shown the superior efficiency of ML approaches over conventional statistical approaches [21]. According to the literature, there is no one-size-fits-all strategy for detecting financial statement fraud [22]. The findings of [23] show that models built using ML approaches can efficiently detect financial statement fraud, keep up with the continuous evolution of financial reporting fraudulent behavior, and respond with the most up-to-date technology. In [24], the authors demonstrate that detection models developed using ML approaches are more accurate than traditional methods. Therefore, our review of past research is limited to papers that have used only ML techniques for FSF detection. Most of the research in the previous literature has formulated the detection of FSF as a binary classification problem, sometimes as a multi-class problem and other times as a clustering problem. Researchers have conducted both quantitative and qualitative FSF analyses. Text mining has been used extensively for qualitative research. Here, we focus on papers that perform quantitative analysis using ML techniques. In the initial stages, research mainly included the Neural Network (NN) [25; 26; 27; 28; 29], LR, DT, SVM, Discriminant Analysis (DA), and Bayesian Belief Network approaches. Supervised learning techniques have been selected for analysis more than unsupervised ones, with studies from the USA, China, Taiwan, and Spain making up 65% of such papers [30]. A considerable number of studies have analyzed the performance of classifiers on FSF detection, showing that SVM [25; 26; 31; 32; 33; 34], NN [35; 36; 37; 38; 39; 40], and DT [41; 42] perform well in FSF detection/prediction. In recent years, ensemble ML techniques have begun to be be used in studies, mostly outperforming single classifiers. Ensemble classifiers integrate the predictions of multiple base models. Numerous empirical and theoretical findings have shown that combining different models can improve predictive accuracy [43]. Moreover, ensemble models are well known for their capability to reduce bias and variance. Many researchers have shown interest in studying ensemble models incorporating boosting [14], bagging [44; 45; 46; 47], and other hybrid methods [48] on both balanced and unbalanced data. It has been determined that the performance of such models depends on the selection of the base classifiers. An illustration of the reviewed papers is provided in Table 1.  
Although prior research shows that ensemble classifiers are best at detecting FSF, there is less research on them compared to single classifiers. Most previous studies have used imbalanced datasets for evaluation, as is the case with real-world data. Consequently, because of the common problem of class imbalance, traditional ensemble models must generally be coupled with sampling techniques such as oversampling or undersampling in order to balance the class distribution. Only a few studies have considered the imbalance issue during modeling. While most researchers have used financial ratios for prediction, others have argued that raw variables produce better results [49; 50; 51]. Various metrics can be used to assess classifier performance, with the prevalent ones being sensitivity or recall,