<ul><li>Generative AI may offer a solution to the challenges faced by the banking sector in using deep learning due to data sensitivity and regulatory constraints.</li><li>This study evaluated five leading models - CTGAN, DGAN, Wasserstein GAN, FinDiff, and TVAE - for generating synthetic financial transaction data.</li><li>While none of the algorithms can replicate the real data's graph structure, each excels in specific areas.</li><li>DGAN is best for privacy-sensitive tasks, FinDiff and TVAE excel in data replication and augmentation, and CTGAN achieves a balance across all criteria.</li></ul>

Generative AI for Banks: Benchmarks and Algorithms for Synthetic Financial Transaction Data

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