Utilizing mathematical concepts of heat map for an intelligent and secure approach to efficiently detect credit card fraud

Abstract

In the current scenario of digital world, every year the financial institutions have to face billions of dollars losses due to fraudulent transactions. Out of different categories of financial frauds credit card transaction fraud is the most common. To reduce the effect of these fraud transactions there is a need for a well-designed and secured fraud detection system with a state of art fraud detection model. Our work’s primary contribution is the creation of a fraud detection system that makes use of some mathematical usage of creating heat maps which is then enhanced with the use of a deep learning architecture and a sophisticated feature engineering method based on HCNN- Heat Map Convolutional Neural Network. HCNN is a model which create the heat maps for the imbalance data set without replicating the minor class records and without discarding major class records. The experimental findings show that our suggested technique is a practical and successful mechanism for detecting credit card fraud. The main objective of our model is to develop such a technique that can be used to detect and correctly classify more numbers of fraud transactions thus, our suggested technique, may detect considerably more fraudulent transactions than the benchmark methods with the accuracy of 91.7%.