Machine Learning based Fraud Analysis and Detection System

Abstract

Abstract The spectacular surge in the proportion of credit card transactions, web based purchases, has led to a surge in fraudulent activities recently. For any business establishment, credit card security is a major concern. In this respect, credit card fraud is hard to identify. Thus it became imperative to implement effectual fraud detection systems for all credit card issuing banks to mitigate their losses. Betrayed transactions with real transactions in actuality are often dispersed and simple methods of matching are not enough to detect them accurately. The paper proposes an algorithm based on Machine Learning credit card fraud detection to solve the issue of a fraudulent transaction. This framework nominally increases the probability of card fraud by exponential activity. The results show that the accuracy of Random Forest, Support Vector Machine and KNN classifiers achieves respectively 94.84%, 89.46%. Random Forest could even predict new fraud cases very quickly.