Credit Card Fraud Detection Using ANN
Abdel Wedoud Oumar1, Peter Augustin D2
1Abdel Wedoud Oumar, Department of Computer Science,CHRIST (Deemed to be University), Bengaluru (Karnataka), India.
2Dr.Peter Augustin D, Department of Computer Science, CHRIST (Deemed to be University), Bengaluru (Karnataka), India.
Manuscript received on 01 May 2019 | Revised Manuscript received on 15 May 2019 | Manuscript published on 30 May 2019 | PP: 313-316 | Volume-8 Issue-7, May 2019 | Retrieval Number: G5211058719/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Fraud on its own was and is devastating a lot of businesses, be them small or large. Particularly in the field of finance where we can see constant attacks on both individuals and enterprises alike. As such, credit cards are the most targeted as they are linked to both personal information and accounts. It is also evident to say that credit card fraud detection research is very much needed to deter and mitigate the impact of fraud on the financial field in general. It is important to identify frauds before it is too late so that the stolen credit card cannot be used for fraudulent transactions. To effectively detect these fraud transactions, we use a data consisting of fraudulent and non-fraudulent transactions to create a model that classifies these transactions with a high accuracy based on a machine learning technique. We used Artificial Neural Network with Logistic Regression to measure and in order to achieve high accuracy, we refined the parameters using the algorithms Back-propagation which has proved to have a high accuracy rate giving the model the ability to distinguish a fraudulent transaction from a normal one.
Keyword: Artificial Neural Networks, Logistic Regression, Backpropagation, Credit Card Fraud.
Scope of the Article: Neural Information Processing.