Machine Learning Model for Credit Card Fraud Detection- A Comparative Analysis
Pratyush Sharma, Souradeep Banerjee, Devyanshi Tiwari, and Jagdish Chandra Patni
School of Computer Science, University of Petroleum and Energy Studies Dehradun
Abstract: In today's world, we are on an express train to a cashless society which has led to a tremendous escalation in the use of credit card transactions. But the flipside of this is that fraudulent activities are on the increase; therefore, implementation of a methodical fraud detection system is indispensable to cardholders as well as the card-issuing banks. In this paper, we are going to use different machine learning algorithms like random forest, logistic regression, Support Vector Machine (SVM), and Neural Networks to train a machine learning model based on the given dataset and create a comparative study on the accuracy and different measures of the models being achieved using each of these algorithms. Using the comparative analysis on the F_1 score, we will be able to predict which algorithm is best suited to serve our purpose for the same. Our study concluded that Artificial Neural Network (ANN) performed best with an F_1 score of 0.91.
Keywords: Machine learning, credit card fraud detection, random forest, accuracy, neural network, SVM.
Received June 10, 2020; accepted February 17, 2021