Open AccessCredit Card Fraud Detection System
Author(s) -
Kartik Madkaikar,
Manthan Nagvekar,
Preity Parab,
Riya Raika,
Supriya Patil
Publication year - 2021
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b6258.0710221
Subject(s) - credit card fraud , credit card , random forest , naive bayes classifier , commission , support vector machine , machine learning , artificial intelligence , computer science , gradient boosting , business , agency (philosophy) , computer security , finance , payment , philosophy , epistemology
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.