Generate Synthetic Data to Train Better Fraud Models

Training robust fraud detection models requires large amounts of diverse data, including examples of known fraud, which may be limited. Generative AI techniques, including LLMs and Generative Adversarial Networks (GANs), can create realistic synthetic claims data, including fraudulent scenarios, based on patterns learned from real data. This augmented dataset can be used to train more accurate and resilient fraud detection models without compromising real customer privacy.