Credit Card Fraud Detection System
Kartik Madkaikar1, Manthan Nagvekar2, Preity Parab3, Riya Raikar4, Supriya Patil5
1Kartik Madkaikar, Student of Bachelor of Engineering, Department of Electronics & Telecommunications Engineering, Padre Conceicao College of Engineering, Verna (Goa), India.
2Manthan Nagvekar*, Student of Bachelor of Engineering, Department of Electronics & Telecommunications Engineering, Padre Conceicao College of Engineering, Verna (Goa), India.
3Preity Parab, Student of Bachelor of Engineering, Department of Electronics & Telecommunications Engineering, Padre Conceicao College of Engineering, Verna (Goa), India.
4Riya Raikar, Student of Bachelor of Engineering, Department of Electronics & Telecommunications Engineering, Padre Conceicao College of Engineering, Verna (Goa), India.
5Dr. Supriya Patil, Associate Professor, Department of Electronics and Telecommunication Engineering, Padre Conceicao College of Engineering, Verna (Goa), India.
Manuscript received on July 14, 2021. | Revised Manuscript received on July 18, 2021. | Manuscript published on July 30, 2021. | PP: 158-162 | Volume-10 Issue-2, July 2021. | Retrieval Number: 100.1/ijrte.B62580710221| DOI: 10.35940/ijrte.B6258.0710221
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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 fraud is a serious criminal offense. It costs individuals and financial institutions billions of dollars annually. According to the reports of the Federal Trade Commission (FTC), a consumer protection agency, the number of theft reports doubled in the last two years. It makes the detection and prevention of fraudulent activities critically important to financial institutions. Machine learning algorithms provide a proactive mechanism to prevent credit card fraud with acceptable accuracy. In this paper Machine Learning algorithms such as Logistic Regression, Naïve Bayes, Random Forest, K- Nearest Neighbor, Gradient Boosting, Support Vector Machine, and Neural Network algorithms are implemented for detection of fraudulent transactions. A comparative analysis of these algorithms is performed to identify an optimal solution.
Keywords: Error Back Propagation Algorithm (EBPA), K-Nearest Neighbor (KNN), Support Vector Machine (SVM).