# A Systematic Review of Intelligent Systems and Analytic Applications in Credit Card Fraud Detection
## 3 Existing Fraud Detection Techniques
### Supervised Learning Models in CCFD  
In CCFD studies, it is also very important to develop model patterns with important features. During the development of the fraud models, SL techniques are frequently used due to the labels of fraud transaction (legal/fraud) [25]. In these studies, SL techniques are employed due to generating predictive results and presenting differential knowledge. Additionally, the performances of SL models can be easily examined with different metrics and approaches with several advantages. As stated in [26], SL techniques give very effective results in terms of model performance in CCFD as opposed to unsupervised learning (USL) algorithms that are using unlabeled data which are also used for fraud detection [27]. Note that some examples of the SL methods and abbreviations are listed in Table 2. \begin{table}
\begin{tabular}{l c} \hline \hline
**Supervised Learning Method** & **Abbreviation** \\ \hline Adaptive Boosting & AdaBoost \\ Adaptive Boosting and Light Gradient Boosting Machine & AdaBoost-LGBM \\ Classification and Regression Tree & CART \\ Category Boosting & CatBoost \\ Convolutional Neural Network & CNN \\ Decision Tree & DT \\ Ensemble Learning & EL \\ Extreme Learning Machine & ELM \\ Extra Tree Classifier & ETC \\ Genetic Algorithm and Support Vector Machine & GA-SVM \\ Generative Adversarial Network & GAN \\ Gradient Boosting Machines & GBM \\ Gaussian Naive Bayesian & GNB \\ Gated Recurrent Unit & GRU \\ K-Nearest Neighbors & KNN \\ Linear Discriminant Analysis & LDA \\ Light Gradient Boosting Machine & LGBM \\ Logistic Regression & LR \\ Long Short-Term Memory & LSTM \\ Long Short-Term Memory and Conditional Random Fields & LSTM-CRF \\ Latent Dirichlet Allocation & LTDA \\ Multilayer Perceptron & MLP \\ Multinominal Naive Bayesian & MNB \\ Naive Bayesian & NB \\ Passive Aggressive Classifier & PAC \\ Particle Swarming Organization and Weighted k Means & PSO-Weighted k-Means \\ Random Forest & RF \\ Radius Neighbors Classifier & RNC \\ Support Vector Machine & SVM \\ eXtreme Gradient Boosting & XGBoost \\ eXtreme Gradient Boosting on Spark & XGBoost-Spark \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Supervised learning methods used in credit card fraud studies. In classification models employing SL, a well-known confusion matrix is taken as the reference to measure and evaluate training performances. In data mining models, many performance metrics, especially accuracy ratio, are calculated using the confusion matrix. The _Accuracy Ratio_ is calculated as given in Equation (1) below. Note that in the equations, _TP_, _TN_, _FP_, and _FN_ represent true positive, true negative, false positive, and false negative [28]. \[Accuracy\ Ratio=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
In addition to the accuracy ratio, it may be useful to calculate and evaluate the sensitivity (known as true positive rate or recall) and specificity (known as true negative rate) metrics. The _Sensitivity_ and _Specificity_ are calculated as given in Equations (2) and (3) below:  
\[Sensitivity=\frac{TP}{TP+FN} \tag{2}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{3}\]  
In CCFD training, Type I and II errors (false positive and false negative) indicate the negative effects on the bank and the bank customer. Model performance is evaluated by calculating the Matthews correlation coefficient (_MCC_) to handle Type I and II errors. When the _MCC_ value is close to 1, the success of the model increases. The _MCC_ is calculated as given in Equation (4) below:  
\[MCC=\frac{\left(TP\times TN\right)-\left(FP\times FN\right)}{\sqrt{\left(TP+ FP\right)\left(TP+FN\right)\left(TN+FP\right)\left(TN+FN\right)}} \tag{4}\]  
In addition to the _MCC_, the binning method is also recommended to eliminate Type 1 and Type 2 errors. For CCFD, the alternative technique with the binning approach has been explained under the emerging fraud detection techniques below. Similarly, the existing fraud detection techniques reviewed (IL, FS and SL) are listed in Table 3.