Credit Card Fraud Detection Using Machine Learning Research Paper

The models built are supervised fraud models that attempt to identify which transactions are most likely fraudulent. Credit card fraud detection using deep learning based on auto encoder and restricted boltzmann machine. Then hybrid methods which use adaboost and majority voting methods are applied.

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In this paper we explore the application of linear and nonlinear statistical modeling and machine learning models on real credit card transaction data.

Credit card fraud detection using machine learning research paper. This paper aims to improve the detection of credit card fraud attacks using long short term memory recurrent neural network lstm rnn with a public dataset. In another paper suman research scholar gjus t at hisar hce presented techniques like supervised and unsupervised learning for credit card fraud detection. Standard models are firstly used. Credit card fraud is a wide ranging issue for financial institutions involving theft and fraud committed using a payment card.

While other researchers have used various methods on publicly available data sets the data set used in this paper is extracted from actual credit card transaction information over three months. In this paper machine learning algorithms are used to detect credit card fraud. Of a variety of machine learning models with a real world credit card data set for fraud detection. International journal of advanced computer science and applications 9 1.

Our proposed model proved to be effective. Even though these methods and algorithms fetched an unexpected success in some areas they failed to provide a permanent and consistent solution to fraud detection.