Supervised learning involves training AI models on labeled data, where the algorithm learns from historical examples of both legitimate and fraudulent transactions. Unsupervised learning, on the other hand, involves detecting anomalies in data without predefined labels. It can identify unusual patterns that might signal fraudulent activities without prior knowledge of specific fraud instances.