CREDIT CARD FRAUD DETECTION USING RANDOM FOREST ALGORITHM

Abstract

Credit card fraud avoidance has been the most popular problem in the developed world. In this case, credit card fraud is identified by fraudulent transactions. Since e-commerce sites are becoming more popular, credit card fraud is becoming more common. When a credit card is stolen it is used for dishonest reasons, a fraudster uses the credit card information for his own purposes, and it is called credit card theft. In order to track online fraud transactions, the new technology employs a variety of methods. To increase the consistency of the proposed scheme, we used a random forest algorithm to find suspicious transactions. It is built on supervise learning algorithm, which classifies the dataset using decision trees. After the dataset has been categorized, a confusion matrix is established. The confusion matrix is used to test the Random Forest Algorithm's accuracy. Keywords— Credit Card, Fraud Detection, Random Forest, Classification technique, Transactions.