Fraud Detection for Online Retail Using Random Forest
R. Abiramy1, Kumar Narayanan2, R. Anandan3, C. Swaraj Paul4
1R. Abiramy, Department of Computer Science & Engineering, Vels Institute of Science Technology & Advanced Studies (VISTAS), Chennai (Tamil Nadu), India.
2Kumar Narayanan, Department of Computer Science & Engineering, Vels Institute of Science Technology & Advanced Studies (VISTAS), Chennai (Tamil Nadu), India.
3R. Anandan, Department of Computer Science & Engineering, Vels Institute of Science Technology & Advanced Studies (VISTAS), Chennai (Tamil Nadu), India.
4C. Swaraj Paul, Department of Computer Science & Engineering, Vels Institute of Science Technology & Advanced Studies (VISTAS), Chennai (Tamil Nadu), India.
Manuscript received on 25 May 2019 | Revised Manuscript received on 03 June 2019 | Manuscript Published on 22 June 2019 | PP: 1-6 | Volume-8 Issue-3S, February 2019 | Retrieval Number: C10010283S19/19©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: The evolution of e-commerce has widely risked the electronic transaction over the past few years. This has significantly raised the issue of fake events worldwide with millions of buck deficits. This work aims to give a solution to frauds done through credit cards. Using datamining and machine learning techniques we provide a highly secured transaction to Web payment gateways (e.g. UPI). General Terms— Data Mining, Decision Tree, Random Forest, Bagging, Filtering Techniques.
Keywords: Electronic Commerce, Credit Card Fraud, Fraud Detection, Online Banking Electronic.
Scope of the Article: e-Commerce