## 2 Related Research
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year \& Ref.** & **Methods** & **Data Source (Fraud: Non-Fraud)** & **Input Features** & **Best Model (Performance in \%)** & **Limitations** \\ \hline
2010 [25] & Probit, NN, LR, SVM & AAER (205:6427) & 23 raw variables & SVM (AUC - 87.8) & Unable to provide adequate fraud detection capabilities, limited features germane to the domain of fraud detection. \\ \hline
2011 [26] & LR, LDA, C4,5, MLP, RBF, SVM & Taiwan Stock Exchange (25:50) & 15 financial ratios + 3 raw variables & SVM (Acc - 92) & Limited selection of input features in analyzing financial statement data. \\ \hline
2011 [31] & SVM, NB, KNN & McGregor-BFA (125:2888) & 14 financial ratios & SVM (Acc-95.9) & The entire data sample was used in both preprocessing and classification algorithm evaluation, and ensemble learning methods are not examined. \\ \hline
2019 [34] & SVM, CART, NN, LR, & Shanghai and Shenzhen Stock exchanges (134:402) & 17 financial ratios + 7 non-financial variables & SVM(Acc-81.88) & This study did not cover overall companies that listed in the Shanghai Stock Exchange and Shenzhen Stock Exchange and may also require some necessary modification when it is applied to other countries. \\ \hline
2011 [37] & LR, SVM, GP, NN & Chinese Stock Exchange (1:1) & 28 financial ratios + 7 raw variables & NN (AUC-98), GP with feature selection (AUC-92.9) & Class imbalance in data set is not handled and limited selection of input features. \\ \hline
2015 [36] & LR, DT, NN & Taiwan and China sources (129:447) & 3 financial ratios + 21 other factors & ANN(Acc-92.8) & Earlier financial statements are difficult to access due to the prolonged study period of time and incomplete samples are eliminated, which may affect the prediction rate. \\ \hline
2016 [42] & DT, BBN, SVM, NN & Taiwan’s listed and OTC companies (44:132) & 21 financial ratios + 2 raw variables + 7 non-financial variables & DT (Acc-87.97) & Class imbalance in data set is not handled and limited selection of input features. \\ \hline
2012 [44] & Probit regression, Logit regression, SGB, RF, Rule ensemble & AAER (114:114) & 12 financial ratios + 1 variable & RF (AUC-90.1) & Test on how this methodology holds up with different sets of fraudulent firms is not analyzed. \\ \hline
2017 [47] & LR, BN, DT, SVM, NN, Bagging, RF, AdaBoost & AAER (311:311) & 24 financial variables + 8 other variables & RF (TP-86.93) & Feature selection is performed using a filter method, and the study is limited by the use of a balanced sample of fraudulent and non- fraudulent firms. \\ \hline
2014 [45] & Logit regression, DT, NN, SVM, Ensemble of LR, DT, NN and SVM & Shanghai and Shenzhen stock exchanges (10:440) & 23 financial ratios & Ensemble (Acc-88.9) & The factors including sample, period and changes in the Chinese economy may influence the prediction modes and is unable to provide adequate fraud detection capabilities. \\ \hline
2018 [46] & SVM, RF, DT, ANN, LR & China Securities Regulatory Commission (120:120) & 17 financial variables+5 non-financial & RF (Acc-75) & The data is not large enough that only Chinese companies contained the variables are not various and innovative. \\ \hline
2020 [14] & LR, SVM, RUSBoost, AdaBoost & AAER (1171:204855) & 28 raw financial variables & RUSBoost (AUC-72.5) & Raw financial variables are only considered. \\ \hline Proposed & XGBoost, LR, DT, SVM, AdaBoost, and RF & Osiris database & 26 financial attributes including financial attributes from Beneish model & Optimized XGBoost (Acc-96.05) & Non-financial attributes are not considered and is treated as future work. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: This is a very wide table. precision, and accuracy [52]. In this paper, we evaluate the different classifiers that can be used in FSF detection while accounting for the class imbalance issue. We take into account both raw financial variables and financial ratios.