Conference article, meeting abstract

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



Publication Details
Authors:
Karst, F.; Chong, S.; Antenor, A.; Lin, E.; Li, M.; Leimeister, J.
Place:
Bangkok, Thailand

Publication year:
2024
Pages range :
15
Book title:
34. Workshop on Information Technologies and Systems, Dec 18, 2024 - Dec 20, 2024
Volume number:
2024
Issue number:
34
Languages:
English


Abstract

The banking sector faces challenges in using deep learning due to data sensitivity and regulatory constraints, but generative AI may offer a solution. Thus, this study identifies effective algorithms for generating synthetic financial transaction data and evaluates five leading models - Conditional Tabular Generative Adversarial Networks (CTGAN), DoppelGANger (DGAN), Wasserstein GAN, Financial Diffusion (FinDiff), and Tabular Variational AutoEncoders (TVAE) - across five criteria: fidelity, synthesis quality, efficiency, privacy, and graph structure. While none of the algorithms is able to replicate the real data's graph structure, each excels in specific areas: DGAN is ideal for privacy-sensitive tasks, FinDiff and TVAE excel in data replication and augmentation, and CTGAN achieves a balance across all five criteria, making it suitable for general applications with moderate privacy concerns. As a result, our findings offer valuable insights for choosing the most suitable algorithm.



Keywords
Financial Services, Generative AI, Synthetic Data


Authors/Editors

Last updated on 2025-20-06 at 09:02