Machine Learning for Credit Card Fraud Detection System
Abstract
The rapid growth of the e-commerce industry has resulted in an exponential increase in the use of credit cards for online purchases, resulting in an increase in associated fraud. In recent years, it has become very difficult for banks to detect fraud in the credit card system. Machine learning plays a key role in detecting credit card fraud in transactions. To predict these transactions, banks are using various machine learning techniques, collecting historical data and using new capabilities to improve their predictive power. The
performance of fraud detection in credit card transactions is highly influenced by the data set sampling approach, the choice of variables, and the detection techniques used. This white paper examines the performance of logistic regression, decision trees, and random forests for credit card fraud detection. The credit card transactions data set was collected by Kaggle and contains a total of 2,84,808 credit card transactions from the European banking data set. We consider fraudulent transactions as a “positive class” and genuine transactions as a “negative class”.
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