Artificial Intelligence based Credit Card Fraud Identification using Fusion Method 
D.Uma Devi1, Gnanaprakasam Thangavel2, P. Anbhazhagan3
1Dr. D. Uma Devi, Associate Professor, Department of IT, Gayatri Vidya Parishad College of Engineering (Autonomous), Visakhapatnam, India.
2Dr. Gnanaprakasam T, Associate Professor, Department of CSE, Gayatri Vidya Parishad College of Engineering (Autonomous), Visakhapatnam, India.
3Dr. Anbhazhagan, Assistant Professor, Department of IT, Gayatri Vidya Parishad College of Engineering (Autonomous), Visakhapatnam, India.
Manuscript received on November 12, 2019. | Revised Manuscript received on November 25, 2019. | Manuscript published on 30 November, 2019. | PP: 4876-4878 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8247118419/2019©BEIESP | DOI: 10.35940/ijrte.D8247.118419
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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: Increase of online transactions has given a greater scope for increasing of credit card frauds. In this work we develop a general framework with Artificial Intelligence based Hadoop. Also that fuses multiple detection algorithms to improve accuracy, reliability. Further to support large amount of transactions storage. The workflow satisfies the design ideas of current credit card fraud identification systems. The verification process for all the transactions is implemented. If incoming transaction that passed through trained model with low probability then it is rejected.
Keywords: Credit Card, Artificial Intelligence, Hadoop, Model Fusion, Fraud Detection.
Scope of the Article: Artificial Intelligence.