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 |
DOI-Link der Erstveröffentlichung: |
Languages: | English |
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