A Novel Approach for Credit Card Fraud Detection Using Deep Learning Algorithms

In today’s dynamic financial landscape, common people to large enterprises use credit cards as a multipurpose tool, where common people use it for managing unexpected bills, making travel plans, and carrying out online transactions with ease on the other hand large enterprises which have complicated financial ecosystems use it for cash flow optimization, spending control, and simplified transactions. As credit card is the source of multiple payments, there is also increasing credit card fraud which causes a lot of loss. To reduce credit card frauds multiple researchers use different algorithms of machine learning for the detection of credit card frauds. In this research, we evaluate the performance of various deep learning algorithms, including Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), and Autoencoder Linear, on a credit fraud detection dataset. By comparing these models and subsequently applying a soft voting classifier, we aim to identify the optimal model for accurate fraud detection.