In recent days customers mostly use credit cards to buy products with the help of credit cards. Information of the customers faces a lot of threats from the entire world from the hackers. Recently the hackers create new processes to hack the credit cards. For this the personal information of the influential customers also started to leak. This paper mainly focuses on the activities of the frauds who try to steal the information and use the credit card by hacking them and the steps which can be taken to prevent this type of theft. The more technology increases the more this problem increases day by day (Fu, et al., 2016). This area of research is significant to detect the activities regarding the theft of the credit card. Hackers use many types of processes to hack the pin number of the credit card by phishing emails or fictitious calls towards the customers. They even create a fake website to attract the customers and from there they also hack the credit cards of the customers. This paper also tries to create awareness to the people so that they can identify the theft or the hacking processes more accurately. The customers who do not know about the whole processes to use credit card properly, they become the easy and soft target of the hackers. The hackers right now do not target the personal information but they target mainly the transaction processes of the credit card of the customers (Roy, et al., 2018). So this paper tries to aware the customers about the insidious activities of the credit card hackers as well as to make them aware about the ways of the hackers through which they can detect the fraudulent activities. The possible solutions and the significance of this problem is also discussed in this paper.
This paper mainly focuses on the major issue of the world and that is the insidious activities on the credit card theft. This paper tries to make a point that can solve the particular fraudulent activities of the credit cards and create awareness to the customers so that they do not do any mistakes regarding this problem.
This research tries to find different ways for the identification of the fraudulent activities in the credit card. To reduce the risk of the credit card theft is also a main focus point of this paper. For the reduction of the fraudulent risk the processes of the advanced methodology and the technology are also discussed in this paper. The management of the risk created by the credit card is mainly discussed here.
In this article the authors use genetic algorithm to solve the problem of the credit card fraud. It has been discussed in the article that the complex computational problems can be solved by these genetic algorithms. The main aim of this paper is to implement the effective fraud detection system and to help the clients to decrease the loss of them (Trivedi and Monika, 2016).
By using genetic algorithm the article firstly tries to detect the processes regarding the fraud activities of the card system. To solve this problem they follow some steps. After completing the steps the fraudulent transactions are identified accurately and the rate of the false alerts decreases.
This system helps to identify or detect the fraud in a very short span. By this process the banks and the customers face a reduced rate of losses and the risk amount also decreases.
The authors try to propose a model for the identification of fraud system in credit card. An interconnected group of artificial neurons mainly forms the artificial neural network system. By implementing this system banks can improve their identification processes of the fraud system. Artificial neural networking system is the best technique in recent times to solve the fraud issue of the credit card. In this paper mainly 3 learning techniques are used to train the neural network system for identification of the fraud system of the theft in the banking system and that are Bayesian Regularization, Gradient descent and LM technique (Behera and Panigrahi 2015).
J. Esmaily and R. Moradinezhad proposed a hybrid model of artificial neural network and decision tree. A two phase approach has been discussed in this type of model. In the first phase classification results of decision tree is done and then in the second phase a dataset is produced by multilayer perception. The false detection rate will increase by using this type of method.
c A credit card fraud detection system by using Smote technique and Whale Optimization technique
Credit card fraud detection system is used mainly to detect the credit card theft. In this research paper the authors mainly two type of algorithm techniques are used. One is whale Optimization Algorithm and another is Synthetic minority oversampling technique (Smote). This Smote technique is used to solve the imbalance form of the classification and the Whale Optimization algorithm used to increase the efficiency of the credit card fraud detection system. Smote technique is mainly used to differentiate between the fraud transaction system and the non fraud detection system. Whale optimization technique is used to detect the error and to optimize the transactions of the fraud (Save, et al., 2017).
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