Abstract

In this article, our primary concentration is on the prevention and detection of fraudulent activity involving credit cards in everyday life. In this instance, the detection of credit card theft is based on fraudulent transactions. In general, fraudulent activity involving credit cards can take place both online and offline. However, in the modern world, fraudulent online transaction activities are growing at an alarming rate day by day. In order to track down fraudulent financial dealings conducted online, the current system makes use of a variety of investigative approaches. In the system that we have proposed, we will be using something called a random forest algorithm (RFA) to determine which transactions are fraudulent and which are accurate. The classification of the dataset is accomplished with the use of decision trees, which are utilized by this approach, which is based on a supervised learning technique. Following the process of the dataset's classification, a confusion matrix is obtained. The confusion matrix is used to measure how well RFA performs in various scenarios.

Description