This course provides a comprehensive exploration of AI-based solutions for credit card fraud detection, emphasizing the implementation and evaluation of advanced algorithms, including Generative Adversarial Networks (GANs). Students will gain practical experience in executing adversarial attacks and optimizing machine learning models, enhancing their ability to develop robust AI systems. Through hands-on projects, participants will synthesize knowledge to address real-world challenges in fraud detection and model resilience.

In this module, we study the background of threats that prevent credit card fraud. Then, we investigate hands-on credit card fraud detection implementations. Also, we discuss metrics to evaluate the performance of credit card fraud detection algorithms.

In this module, we study generative adversarial networks (GANs) background. Then, we investigate a hands-on GAN implementation and how it can be used to develop synthetic data likely indistinguishable from the real data.

In this module, we will discuss black and white-box adversarial attacks. Also, we will explore hands-on implementations of several adversarial attacks.

In this module we will study reinforcement learning (RL) and how it can be used for adversarial attacks. Also, we will study data engineering techniques to optimize datasets to help improve ML model performance.

In this module, we will discuss feature engineering and model optimization techniques. Also, we will explore ML model performance metrics.