Balancing Data with Gen AI: Credit Card Fraud Detection

In this 2-hour instructor-led project, you will learn how to use Generative AI to generate data to address data imbalance. SecureTrust Financial Services, a financial institution, asked us to help them improve the accuracy of their fraud detection system. The model is a binary classifier, but it is failing due to data imbalance. As data scientists, we will use Generative Adversarial Networks (GANs), a subset of Generative AI, to create synthetic fraudulent transactions that closely resemble real transactions. This approach aims to balance the dataset and improve the accuracy of the fraud detection model. This project is for anyone interested in learning how Generative models can improve model accuracy by generating synthetic data. To get the most out of this project, it is recommended to have at least one year of experience using deep learning frameworks such as TensorFlow and Keras in Python.