Data Balancing with Gen AI: Credit Card Fraud Detection

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Overview

Data Balancing with Gen AI: Credit Card Fraud Detection is a focused, hands-on course that introduces learners to solving one of the most common challenges in fraud detection—imbalanced datasets—using Generative AI techniques. Fraudulent transactions are rare but highly impactful, and traditional models often struggle to detect them due to skewed class distributions. This course provides a practical approach to balancing data using advanced AI methods for more accurate fraud prediction. Participants will learn about class imbalance, its impact on machine learning...

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Project Overview

A financial institution, called SecureTrust Financial Services, has contracted us to improve the accuracy of their fraud detection machine learning model. The model is a binary classifier, but it is not working well because the data is imbalanced. To solve this problem as data scientists, we will use generative adversarial networks (GANs), a type of Generative AI, to generate synthetic fraudulent transactions that are indistinguishable from real transactions. This will to balance the dataset and improve the accuracy of the fraud detection model.