Design and Implementation of Different Machine Learning Algorithms for Credit Card Fraud Detection

It is essential that credit card companies are able to detect fraudulent transactions so that customers are not charged for items they did not purchase. Data can be used to solve these issues. Science and its importance, as well as machine and soft learning, could not be more critical. When someone defrauds you of your money or otherwise harms your financial well-being through deception or other illegal means, this is referred to as financial fraud. Billions of dollars worth of financial fraud is committed every year. According to the Federal Trade Commission(FTC), the number of theft reports has more than doubled in the last two years. One of the major types of financial fraud is credit card fraud. As the number of online transactions is growing, so is the number of credit card frauds. An effective solution is necessary to reduce loss due to fraudulent transactions at the initial stage. An effective way to do so would be to use machine learning algorithms to detect credit card fraud. This paper examines latest advances and application in the field of machine learning-based credit card fraud detection. In this paper four machine learning algorithms have been analyzed and compared on the basis of their accuracies. It is found out that Catboost algorithm works best to detect credit card fraud with an accuracy of 99.87 percentage. The dataset for credit card fraud detection was taken from kaggle.