# Financial Fraud Detection Based on Machine Learning: A Systematic Literature Review
## 3 Search Results and Meta-Analysis
### Synthesis Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fraud Type** & **Description** & **Technique Used** & **References** & **No. of** \\ \hline \multirow{8}{*}{Credit Card Fraud} & \multirow{8}{*}{Illegitimate use of the card without proper owners’ authorization} & Support Vector Machine & [18, 57, 65, 66, 67] & \\ \cline{3-4}  & & Fuzzy logic & [68, 69] & \\ \cline{3-4}  & & Clustering based method, & [70, 71] & \\ \cline{3-4}  & & Artificial Neural Network & [70, 72] & \\ \cline{3-4}  & & Hidden Markov model & [20, 32, 73] & \\ \cline{3-4}  & & Decision Tree & [16, 21, 74] & \\ \cline{3-4}  & & Genetic Algorithm & [19, 75] & \\ \cline{3-4}  & & Artificial Neural Network & [30, 61, 76, 77] & \\ \cline{3-4}  & & Naive Bayes & [5, 28, 69, 78] & \\ \cline{3-4}  & & Logistic Regression & [78] & \\ \cline{3-4}  & & Random Forest & [29, 69, 79] & \\ \hline \multirow{8}{*}{Health Insurance Fraud} & \multirow{8}{*}{Fraudulent claims by individuals or organizations to support the relevant expenses of theft or accidental damages.} & Support Vector Machine & [80] & \\ \cline{3-4}  & & Artificial Neural Network & [81] & \\ \cline{3-4}  & & K Nearest Neighbors & [82] & \\ \cline{3-4}  & & Naive Bayes & [83] & \\ \cline{3-4}  & & Clustering-based method, & [40] & \\ \hline Auto Insurance Fraud & Fraudulent claims by an individual to get health insurance profits. & Support Vector Machine & [41, 42] & \\ \cline{3-4}  & & K Nearest Neighbors & [84] & \\ \hline Cyber Financial fraud & Financial fraudulent activities through cyber space & Artificial Neural Network & [47, 48, 49, 50] & 3 \\ \cline{3-4}  & & SVM & [50, 85] & 2 \\ \hline \multirow{8}{*}{Others} & \multirow{8}{*}{Other frauds that are faced in the financial domains include commodities and securities fraud [32], mortgage fraud, corporate fraud, and money laundering.} & Support Vector Machine & [42] & \\ \cline{3-4}  & & Decision Tree & [42] & \\ \cline{3-4}  & & CoCoA & Fuzzy logic & [69] & \\ \cline{3-4}  & & Clustering-based method, & [51] & \\ \cline{3-4}  & & Hidden Markov model & [86] & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: \(Cont.\)In this approach, the authors examined the model performance using commercial banks' business datasets. The results demonstrated that the SVM outperformed other compared models, thereby confirming its viability. [PERSON] and [PERSON] [57] presented an approach for detecting fraudulent behaviors in credit cards based on SVM techniques. The authors combine a hybrid SVM and spike detections to address the limitations of existing methods. It is proven that the result of the proposed approach outperformed existing methods. [PERSON] and [PERSON] [67] developed an SVM-based supervised learning approach to distinguish between illegal and legal customer behaviors in credit card transactions. To increase the accuracy detection rate, the authors combined SVM, logic, and linear regression methods. A study by [PERSON] et al. [41], which utilized a one-class SVM-based under-sampling technique, was presented to enhance fraud detection in insurance industries. A study in [66] presented a fraudulent detection method for credit card transactions by integrating OSVM with a deep learning approach. The method was assessed using real-world datasets, and the results showed promising results.