Credit Card Fraud Detection using GAN

Credit Card Fraud Detection using GAN

U. Anjana Naidu

Computer Science and Engineering

Sasi Institute of Technology and Engineeering

Tadepalligudem, INDIA

J.Vasantha Siva Nageswari

Computer Science and Engineering

Sasi Institute of Technology and Engineeering

Tadepalligudem, INDIA

K.Saion

Computer Science and Engineering

Sasi Institute of Technology and Engineeering

Tadepalligudem, INDIA

Dr.M.Satya Srinivas

Computer Science and Engineering

Sasi Institute of Technology and Engineeering

Tadepalligudem, INDIA

N.S.V.V.N.Pavana kalyan

Computer Science and Engineering

Sasi Institute of Technology and Engineeering

Tadepalligudem, INDIA

           Abstract -- Credit card fraud detection is a critical issue in the financial industry, requiring robust and efficient methods to identify and prevent fraudulent transactions. This survey paper reviews various machine learning (ML) and deep learning (DL) techniques employed in credit card fraud detection, highlighting  the performance and limitations of each method. Identifying credit card fraud remains a pressing concern for financial systems, demanding innovative and reliable detection strategies. Classic machine learning techniques, including Random Forest, Support Vector Machines, and K-Nearest Neighbors, have delivered encouraging outcomes, with accuracy peaking at 94.84% in controlled settings.generative adversarial networks (GANs), have demonstratedsuperior performance. Moreover, the paper discusses the robustness of hybrid models like AdaBoost and ensemble learning in real     world scenarios, even under noisy conditions.