Comparative Evaluation of Credit Card Fraud Detection Using Machine Learning Techniques

Credit card fraud is a serious and growing problem with the increase in e-commerce and online transactions in this modern era. With this identity theft and loss of money, such mischievous practices can affect millions of people around the world. Criminal activity is a rising threat to the financial sector with-reaching implications. Information extraction seemed to have assumed a basic job in recognition of online payment fraud, fraud detection efficiency in credit card purchases is significantly affected by the data set measuring strategy, the choice of variable and the detection techniques used. This publication inspects execution of, Support Vector Machine, Naive Bayes, Logistic Regression and K-Nearest Neighbor on exceptionally distorted data on credit card fraud. The execution of these techniques is assessed dependent on accuracy, sensitivity, precision, specificity. The outcomes show an ideal accuracy for logistic regression, Naive Bayes, k-nearest neighbor and Support vector machine classifiers are 99.07%, 95.98%, 96.91%, and 97.53% respectively. The relative outcomes demonstrate that logistic regression performs superior to other algorithms.