Beitrag zu einer Konferenz, Meeting Abstract

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



Details zur Publikation
Autor(inn)en:
Karst, F.; Chong, S.; Antenor, A.; Lin, E.; Li, M.; Leimeister, J.
Verlagsort / Veröffentlichungsort:
Bangkok, Thailand

Publikationsjahr:
2024
Seitenbereich:
15
Buchtitel:
34. Workshop on Information Technologies and Systems, Dec 18, 2024 - Dec 20, 2024
Jahrgang/Band :
2024
Heftnummer:
34
Sprachen:
Englisch


Zusammenfassung, 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.



Schlagwörter
Financial Services, Generative AI, Synthetic Data


Autor(inn)en / Herausgeber(innen)

Zuletzt aktualisiert 2025-20-06 um 09:02