Credit Card Fraud Detection Using Machine Learning

This research explores the application of Machine Learning (ML) algorithms in the detection of credit card fraud. Credit card fraudulence is a major concern that poses a significant threat to the financial industry, and machine learning has emerged as a promising approach to addressing this issue. The motive of this study is mainly to evaluate the efficacy of ML algorithms in the detection of credit card fraudulence and, to identify the key elements that impact the accuracy of these ML algorithms. The study utilizes a rigorous experimental design and a large sample size, which enables the analysis of the performance of diverseML algorithms. However, the choice of ML algorithm and the performance evaluation metrics that is used are the important factors that influence the accuracy of the algorithms. This study also identifies the key factors that influence the accuracy of machine learning algorithms, including the choice of features, the quality of the data, and, also the parameter settings of the algorithms.