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

2024/12/19 by Fabian Karst, Sook-Yee Chong, Karst, Fabian Sven +9 · 1 citation
Decision Sciences · Economics, Econometrics and Finance · Business, Management and Accounting · #Stock Market Forecasting Methods #Banking stability, regulation, efficiency #Financial Distress and Bankruptcy Prediction

paper · pdf · doi:10.48550/arxiv.2412.14730

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.

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