IRJET- Comparative Analysis of Credit Card Fraud Detection Using Machine Learning and Deep Learning Techniques (original) (raw)

Credit Card fraud is a sort of identity theft where thieves acquire or receive cash advances from another user's credit card account. This may occur through the use of a user's current accounts, physical credit card robbery, account number or PINs, or through the opening of an unknown credit card account in the user name. The Credit Card fraud detection project identifies the fraudulent nature of the new transaction by shaping the credit card transactions with the knowledge of those which have been fraudulent. In order to detect, if a transaction is a normal payment or a fraud, we will employ several predictive models. The strategies for classification are promising ways to identify fraud and non-fraud transactions. Sadly, classifying techniques do not work well in certain circumstances when it comes to big disparities in data distribution. In our work, we will be applying Machine-Learning algorithms: Logistic Regression, SVM, Naive Bays, Decision Trees, Random Forests and Deep Learning algorithm to predict fraud through Artificial Neural Networks. Results are analysed and compared.

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