Credit Card Fraud Detection using Hybrid Machine Learning Algorithm

Abstract

Credit card security is one of the needful criteria nowadays, which can cause billions of economic frauds worldwide. Some security firewalls provided by the banks are not up to the criteria in the daily life of the common user. The present work focuses on the fraud detection areas in data links and the scope of finding the accuracy to eliminate fraud while using. Data security with machine learning objectives needs more promising algorithms to improve accuracy; the hybridization of algorithms is a new era where two methods can evolve to find solutions for e-commerce applications. This paper combines the random forest and honeybee algorithms from machine learning to detect fraud. Combining the Random Forest Algorithm with the Honey Bee Algorithm, we developed the best model. Different types of hybridization and algorithms in the credit card space should be the subject of future research