Designed Methodologies to Recognize Credit Card Deceptions with Machine and Deep Learning Techniques
Naziya Shaik1, Priyanka Sanikommu2, Suhasini Sodagudi3
1Naziya Shaik, Department of Information Technology, VR.Siddhartha Engineering College, Vijayawada (Andhra Pradesh), India.
2Priyanka Sanikommu, Department of Information Technology, VR.Siddhartha Engineering College, Vijayawada (Andhra Pradesh), India.
3Suhasini Sodagudi, Department of Information Technology, VR.Siddhartha Engineering College, Vijayawada (Andhra Pradesh), India.
Manuscript received on 01 May 2019 | Revised Manuscript received on 15 May 2019 | Manuscript published on 30 May 2019 | PP: 2113-2117 | Volume-8 Issue-7, May 2019 | Retrieval Number: G6093058719/19©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: It is well heard and invisible problem of different kinds of deceptions happening with the rise of ecommerce technology developments. Specifically, such problems need to be addressed and controlled. This paper presents various implementation techniques that are necessary for identifying irregularities in the usage of card systems. The ultimate outcome is to identify the finances loss anomaly. In view of the current state of art, the problem is considered to address with machine and deep learning methods. To eradicate fraudulency, it is proposed to apply random forest, SVM of machine learning techniques and CNN deep learning method. The comparative analysis of the proposed methods is discussed in the paper. The performance study of the proposed techniques is also covered.
Keyword: Card Systems, Deceptions, Irregularities, Machine Learning.
Scope of the Article: Deep Learning.