ABSTARCT :

Recent advancements in electronic commerce and communication systems have significantly increased the use of credit cards for both online and regular transactions. However, there has been a steady rise in fraudulent credit card transactions, costing financial companies huge losses every year. The development of effective fraud detection algorithms is vital in minimizing these losses, but it is challenging because most credit card datasets are highly imbalanced. Also, using conventional machine learning algorithms for credit card fraud detection is inefficient due to their design, which involves a static mapping of the input vector to output vectors. Therefore, they cannot adapt to the dynamic shopping behaviour of credit card clients. This paper proposes an efficient approach to detect credit card fraud using a neural network ensemble classifier and a hybrid data resampling method. The ensemble classifier is obtained using a long short term memory (LSTM) neural network as the base learner in the adaptive boosting (AdaBoost) technique. Meanwhile, the hybrid resampling is achieved using the synthetic minority oversampling technique and edited nearest neighbour (SMOTE-ENN) method. The effectiveness of the proposed method is demonstrated using publicly available real-world credit card transaction datasets. The performance of the proposed approach is benchmarked against the following algorithms: support vector machine (SVM), multilayer perceptron (MLP), decision tree, traditional AdaBoost, and LSTM. The experimental results show that the classifiers performed better when trained with the resampled data, and the proposed LSTM ensemble outperformed the other algorithms by obtaining a sensitivity and specificity of 0.996 and 0.998, respectively.

EXISTING SYSTEM :

It is not sufficient to base the superior performance of our proposed method on the comparison with conventional algorithms. However, it is necessary to compare our approach with existing credit card fraud detection methods in the literature. The methods include the following: the sequential combination of C4.5 decision tree and naïve Bayes (NB) [5], a light gradient boosting machine (Light GBM) with a Bayesian-based hyperparameter optimization algorithm [14], a light gradient boosting machine (Light GBM) with a Bayesian-based hyperparameter optimization algorithm [14], a cost-sensitive SVM (CS SVM) [6], an optimized random forest (RF) classifier [34], a deep neural network (DNN) [35], a random forest classifier with SMOTE data resampling [36], an improved AdaBoost classifier with principal component analysis (PCA) and SMOTE method [37], a cost-sensitive neural network ensemble (CS-NNE) [38], a stochastic ensemble classifier operating in a discretized feature space [39], a model based on overfitting-cautious heterogeneous ensemble (OCHE) [40], a dynamic weighted ensemble technique using Markov Chain (DWE-MC) [41], and an extreme gradient boosting (XGBoost) ensemble classifier with SMOTE resampling technique.

PROPOSED SYSTEM :

            This research utilizes the well-known credit card fraud detection dataset [20]. The dataset was prepared by the Université Libre de Brucellas (ULB) Machine Learning Group on big data mining and fraud detection [9]. The dataset contains credit card transactions performed within two days in September 2013 by European credit card clients. The dataset is imbalanced, with only 492 fraudulent transactions out of 284 807. Meanwhile, all the attributes except ‘‘Time’’ and ‘‘Amount’’ are numerical due to the transformation carried out on the dataset, and they are coded as V1, V2, . . . , V28 for confidentiality reasons. The ‘‘Amount’’ attribute is the cost of the transaction and the ‘‘Time’’ attribute is the seconds that elapsed between a transaction and the first transaction in the dataset. Lastly, the attribute ‘‘Class’’ is the dependent variable, and it has a value of 1 for fraudulent transactions and 0 for legitimate transactions.

SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10

HARDWARE REQUIREMENTS:

 Processor : Core I3
 RAM Capacity : 2 GB
 Hard Disk : 250 GB
 Monitor : 15″ Color
 Mouse : 2 or 3 Button Mouse
 Key Board : Windows 08/10