
Cited Used  Data Description  Applied Method  Validation Method  Advantage  Limitation 

[27]  Credit and debit card transactions of the Spanish bank BBVA, from January 2011 to December 2012  Multilayer perceptron (MLP)  True Positive Rate (TPR), Receiver Operating Characteristic (ROC curves)  The improvement of accuracy in detecting results by using parenclitic networks reconstruction for feature extraction  Requires the cases where the features are not correlated or not extracted by parenclitic networks 
[28]  Transactions of National banking group of Italy, from April 2013 to August 2013  Multiobjective genetic algorithm  TPR, ROC curves  Provide feature selection process via the autotuning method  Should be applied to cases other than Banksealer 
[29]  UCSD Data Mining Contest 2009 data  Deep neural network (DNN)  Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Errors (MAE), Root Mean Squared Log Error (RMSLE)  Study on the importance of features based on the deep learning method  Does not have accurate experimental explanation process and validation 
[30]  Dataset achieved from the second robotic & artificial intelligence festival of Amirkabir University  Decision trees  FMeasure
 Ensemble classification is performed using costsensitive decision trees in a decision forest framework  Having a class imbalance problem 
[31]  German dataset (which has been used in KDD99 competition), Australian credit cards’ open dataset  Particle swarm optimization (PSO), Teachinglearning based optimization (TLBO)  Confusion Matrix (True positive, True negative, False positive, False negative)  Experiment with various datasets
 Detection accuracy is relatively low 
[32]  Not specific  Linear Regression, Artificial Neural Networks (ANN), KNearest Neighbor (KNN), Support Vector Machine (SVM), Decision Stump, M5P Tree, Decision Table  Normalized Root Mean Squared Error (NRMSE), TPR, FMeasure  A comparative study using various algorithms  Detection accuracy should be increased 
[33]  UCI German credit card dataset  SVM  Kfold Cross validation  As Gaussian kernels including RBF are with appropriate regularization, it guarantees a globally optimal predictor which minimizes both the estimation and approximation errors of a classifier  There is no comparison with other algorithms and there is no explanation to verify SVM algorithms superiority to others 
[34]  Credit card transaction data from commercial bank in China  Convolutional Neural Networks (CNN), KMeans  FMeasure  Designing a feature called trading entropy based on the latest consumption preferences for each customer and generating synthetic fraudulent samples from real frauds by a costbased sampling method  Detection accuracy should be increased 
[35]  Banking transaction dataset in Iran  KNN  Accuracy, Recall, Precision  A novel approach combining Knearest neighbor, association rules like Apriori algorithm  The validation is not specific and it is difficult to compare the proposed results with other algorithms 
[36]  Open dataset: ccFraud  NN, PSO, Autoassociative neural network (AANN), Particle swarm optimization autoassociative neural network (PSOAANN)  MSE, Classification Rate (CR)  Combined parallelization of the autoassociative neural network in the hybrid architecture  Dataset is highly unbalanced and detection accuracy should be increased 
[37]  Open dataset: MIT Human Dynamics Lab  SVM, Fuzzy clustering  TPR, FPR, ROC curves  Divide the fraud detection system into two principal modules  Would be better to compare it with more diverse algorithms 
[38]  Transactions from a large national bank in Italy, collected from December 2012 to August 2013  Principal component analysis (PCA), DBSCAN  ROC curves  Operate in online processing  Accuracy by validation is not constant 
[39]  Not specific  Self organizing map (SOM)  TPR, FPR  Division of transactions to form an input matrix and ability to be applied to a large complex set  Only have compared to one algorithm 
