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
