COMPARATIVE ANALYSIS OF ALGORITHMS USED IN CREDIT CARD FRAUD DETECTION SYSTEM

Machine learning is an application of Artificial Intelligence which is the scientific study of algorithms and that computer use in order to perform a specific given task without making use of explicitly provided instructions and completely relying on patterns and inference instead. This kind of learning is best suited for making predictions and decisions without programming them explicitly. Using machine learning one can analyze different kinds of data and draw conclusions from them. In this paper The Credit Card Fraud Detection is taken care of. This includes considering previous credit card transactions by keeping in mind the ones that usually turns out to be fraud. This model is later on used to identify if a new transaction is fraudulent or not. The paper aims to detect hundred percent of the fraudulent transactions that are occurring along with bringing down the incorrect fraud classifications. For finding out the fraudulent transactions different algorithms are made used of. A comparative study between Random forest, SVM, Naïve Bayes, LOF is performed.