Currently, there is a growth in online transactions which has led to the immerse growth of the number of credit card fraud. A lot more people are opting to shop online due to convenience and therefore they make online payments to make a purchase that would be delivered to them and in some cases, they make payments online for a service rendered to them. With such an opportunity, fraudsters are also increasing their fraud activities online. Therefore, this study seeks to detect credit card fraud using an adaptive tool and also attempts to reduce the number of wrongly predicted valid transactions made by the model. Researchers have used tools such as K-nearest neighbour, logistic regression, random forest, decision trees and others however, this study uses an autoencoder neural network to detect credit card fraud. The study then evaluates the model using an appropriate evaluation metric.
Keywords: Fraud detection, adaptable, autoencoder neural network, credit card, online transactions
