Credit Card Fraud Detection and Prevention using Machine Learning
S. Abinayaa1, H. Sangeetha2, R. A. Karthikeyan3, K. Saran Sriram4, D. Piyush5
1S. Abinayaa*, Assistant Professor, SRM Institute of Science and Technology, Ramapuram, Tamilnadu, Chennai.
2H. Sangeetha, Assistant Professor, SRM Institute of Science and Technology, Ramapuram, Tamilnadu, Chennai.
3R. A. Karthikeyan, B. Tech, Information Technology, SRM Institute of Science and Technology, Ramapuram, Tamilnadu, Chennai.
4K. Saran Sriram, B. Tech, Information Technology, SRM Institute of Science and Technology, Ramapuram Tamilnadu, Chennai.
5D. Piyush, B. Tech Information Technology, SRM Institute of Science and Technology, Ramapuram Tamilnadu, Chennai.
Manuscript received on March 30, 2020. | Revised Manuscript received on April 05, 2020. | Manuscript published on April 30, 2020. | PP: 1199-1201 | Volume-9 Issue-4, April 2020. | Retrieval Number: D7327049420/2020©BEIESP | DOI: 10.35940/ijeat.D7327.049420
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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: This research focused mainly on detecting credit card fraud in real world. We must collect the credit card data sets initially for qualified data set. Then provide queries on the user’s credit card to test the data set. After random forest algorithm classification method using the already evaluated data set and providing current data set. Finally, the accuracy of the results data is optimised. Then the processing of a number of attributes will be implemented, so that affecting fraud detection can be found in viewing the representation of the graphical model. The techniques efficiency is measured based on accuracy, flexibility, and specificity, precision. The results obtained with the use of the Random Forest Algorithm have proved much more effective.
Keywords: Accuracy, Fraud Detection, Precision, Random Forest Algorithm, Sensitivity.