# Financial Fraud Detection Based on Machine Learning: A Systematic Literature Review
## 3 Search Results and Meta-Analysis
### Clustering Based Methods
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Techniques** & **Short Description** & **No. of Articles** & **References** \\ \hline SVM & A classification method used in linear classification & 10 & [18, 41, 42, 54, 57, 65, 66, 66, 80, 80] \\ HMM & A dual embedded random process used to provide & 8 & [19, 20, 73, 74, 86, 89, 94, 103] \\ ANN & Amulti-layer network that works similar to & 10 & [28, 30, 33, 61, 62, 75, 76, 77, 81, 97] \\ Fuzzy Logic & A logic that indicates that methods of thinking are & 5 & [68, 69, 91, 92] \\ KNN & It classifies data according to their similar and & 7 & [60, 70, 72, 78, 84, 98, 99] \\ Decision Tree & A regression tree and classification method that is & 5 & [29, 44, 54, 57, 67] \\ Genetic Algorithm & It searches for the best way to solve problems & 3 & [35, 61, 71] \\ Ensemble & Meta algorithms that combined manifold & 8 & [2, 29, 33, 64, 69, 79, 98, 105], \\ Logistic Regression & They are mainly applied in binary and multi-class & 8 & [35, 78, 81, 104, 106, 107, 108] \\ Clustering & Unsupervised learning method which involve & 6 & [5, 18, 56, 102, 103] \\ Random Forest & Classification methods that operate by combining & 7 & [21, 29, 67, 79, 109, 110, 111] \\ Naive Bayes & A classification algorithm that can predict & 11 & [5, 28, 31, 33, 61, 67, 78, 83, 101, 101, 112] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: ML techniques used for financial fraud detection. The model's accuracy measures how many of the model's overall predictions were accurate, whereas precision measures how accurate the model's positive predictions were [42, 69, 82]. The percentage of positive cases that the classifier properly detects is known as the recall of a classifier, also known as sensitivity [21, 67, 113]. The classifier's specificity is measured as a ratio of correctly classified negative samples to all negative samples [78, 82, 113]. While it makes sense to increase both recall and precision, the two metrics have an inverse relationship. Lower recall may arise by forcing a higher precision, and vice versa [78, 82, 113]. This is referred to as the recall/precision trade-off. The F-score, which is the harmonic mean of the precision and recall is a better metric to maximize instead [21, 56, 113]. It is possible to track the performance change in terms of the trade-off between certain measures such as recall and precision by tweaking the decision threshold value for a classifier. This process of evaluating all confusion matrices that can result from changing the decision threshold is known as parametric evaluation. Another metric is the Receiver Operating Characteristic (ROC) curve and the precision-recall curve [35, 113]. The area under the ROC curve (AUC) can be used to compare classifiers, with perfect classifiers possessing an AUC equal to 1 and purely random classifiers having an AUC equal to 0.5. Different distance measures, such as the mean squared error (MSE), Euclidean distance, Manhattan distance, and others, are used by clustering algorithms to quantify the similarity or dissimilarity of samples or observations. These algorithms group similar data points based on relevant features. Table 7 presents the formulas of performance measures with their corresponding references and the number of studies for each metric. As for the FPR and FNR, the lower the value of the performance measures, the more improved the generalization ability of models. In this table, TP, TN, FP, and FN stand for the true positive, true negative, false positive, and false negative, respectively.