Machine Learning for Detecting Credit Card Frauds
Atika Gupta1, Bhaskar Pant2, Nidhi Mehra3, Divya Kapil4
1Atika Gupta, Department of Computing, Graphic Era Hill University, (Uttarakhand), India.
2Dr. Bhaskar Pant, Department of CSE, Graphic Era Deemed to be University, (Uttarakhand), India.
3Nidhi Mehra, Department of Computing, Graphic Era Hill University, (Uttarakhand), India.
4Divya Kapil, Department of Computing, Graphic Era Hill University, (Uttarakhand), India.
Manuscript received on 15 June 2019 | Revised Manuscript received on 22 June 2019 | Manuscript Published on 01 July 2020 | PP: 16-23 | Volume-8 Issue-2S12 September 2019 | Retrieval Number: B10030982S1219/2020©BEIESP | DOI: 10.35940/ijrte.B1003.0982S1219
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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: Credit card frauds has been a threat that has evolved as a major source of loss for the financial sectors. It has been seen in the different parts of world causing loss of billions of dollars. It is also a area which needs attention from the researchers as the task of fraud detection can be automated using the different machine learning classifiers and data science. If the frauds model encounter the fraudulent transactions it will raise an alarm to the system administrator. The paper proposes a model which uses the machine learning classifiers to detect the fraudulent transactions. The classifiers used in the paper are SVM (Support Vectore Machine ), Isolation Forest and Local Outlier. The focus of the research is to detect the fraudulent transactions to 100% and also we emphasise on the fact that no normal transaction should be detected as fraud wrongly. The process starts with preprocessing the data and then the classifers are applied. The results from each classifers is evaluated to check the one with the better performance. The performance can be increased with use of deep learning algorithms but with the rise in expennses.
Keywords: Credit Card Fraud, Machine Learning, Isolation Forest, Local Outlier.
Scope of the Article: Machine Learning