## 1 Introduction
### Motivation and Contribution  
Globally, the cost of FSF has continued to rise over the years. The most direct victims are the investors and financiers who supply funds to firms under false pretenses. There are greater costs to society at the macro-level, such as loss of trust in financial systems, even though the economic impact at the company level can be highly variable. Because FSF causes significant property harm to investors, stakeholders, and society, a great number of studies have been conducted. Most of the works in the literature on FSF apply traditional regression analysis; see, for instance, [PERSON] and [PERSON] [6; 7; 8; 9; 10]. In recent years, a number of experts and researchers have attempted to use machine learning (ML) and data mining methods to carry out research in this field as a way of reducing detection errors. While many business strategies are based on the accuracy of financial statements, there are not enough resources to analyze all of them thoroughly. As auditors have been found culpable in multiple examples of FSF, credible financial fraud detection models should be offered in an easy-to-use manner to auditors, investors, regulators, and other stakeholders. Because of the inherent reliance on limited distributional assumptions, parametric techniques such as LR lack the general applicability that non-parametric approaches may provide [11]. Existing research on quantitative methods of financial fraud detection, on the other hand, is primarily focused on the banking and financial services industry, mostly on the detection of insurance and credit card fraud. Quantitative techniques to identify and discourage FSF should receive significant attention in respected academic journals. The current scientific and academic literature currently seeks further rules or classifications from previous data to achieve the purpose of prediction or detection. Machine learning (ML) with an appropriate amount of data can yield more accurate prediction and classification outcomes compared to the traditional approach. Contribution: This study aims to establish a superior model for financial fraud prediction using a powerful ensemble technique, the XGBoost (eXtreme Gradient Boosting) algorithm, to help identify fraud on a set of sample companies drawn from the MENA region by spotting the early signs. This approach can help to mitigate losses to investors, auditors, and all stakeholders in the financial market. This research enables researchers and practitioners to gain a deeper understanding and make informed decisions based on a financial statement fraud detection model. We focus on utilizing data from publicly available financial statements of firms in the Middle East and North Africa (MENA) region. We selected a powerful ML ensemble model, namely, the XGBoost algorithm, to model our proposed method for a number of reasons. Ensemble algorithms have been successfully used in many fields of research [12], though they have been less utilized in financial fraud studies. The characteristics of XGBoost fit very well with our small dataset, which is characterized by many missing values and high class imbalance. XGBoost facilitates the tuning of a range of hyperparameters in order to further improve the efficiency of the model. Because of the high class imbalance in this one-of-a-kind domain, the samples selected in past studies have a tendency to be processed less realistically. XGBoost is able to effectively learn from imbalanced data with the help of Synthetic Minority Oversampling Technique (SMOTE) sampling. We chose expert-defined financial ratios [13] along with raw financial data for our research. FSF detection models based on financial ratios may be more effective, as the ratios determined by domain experts are mostly based on assumptions that provide a strong prediction of situations in which corporate managers are encouraged to commit fraud [14].