Isolation Forest and Local Outlier Factor for Credit Card Fraud Detection System 
V. Vijayakumar1, Nallam Sri Divya2, P. Sarojini3, K. Sonika4
1Dr. V. Vijayakumar*, Computer Science and Engineering Department, Sri Manakula Vinayagar Engineering College, Puducherry, India.
2Nallam Sri Divya, Computer Science and Engineering Department, Sri Manakula Vinayagar Engineering College, Puducherry, India.
Manuscript received on April 05, 2020. | Revised Manuscript received on April 17, 2020. | Manuscript published on April 30, 2020. | PP: 160-165 | Volume-9 Issue-4, April 2020. | Retrieval Number: D6815049420/2020©BEIESP | DOI: 10.35940/ijeat.D6815.049420
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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 identification is a crucial issue facing large economic institutions, which has caused due to the rise in credit card payments. This paper brings a new approach for the predictive identification of credit card payment frauds focused on Isolation Forest and Local Outlier Factor. The suggested solution comprises of the corresponding phases: pre-processing of data-sets, training and sorting, convergence of decisions and analysis of tests. In this article, the behavior characteristics of correct and incorrect transactions are to be taught by two kinds of algorithms local outlier factor and isolation forest. To date, several researchers identified different approaches for identifying and growing such frauds. In this paper we suggest analysis of Isolation Forest and Local Outlier Factor algorithms using python and their comprehensive experimental results. Upon evaluating the dataset, we received Isolation Forest with high accuracy compared to Local Outlier Factor Algorithm.
Keywords: Anomaly detection, isolation, local outlier, fraudulent, credit card.