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

arXiv: https://arxiv.org/abs/2412.14730 · HTML: https://mlsanity.com/paper/2412.14730/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 2 references that are in the map.
- First posted on arXiv 19 Dec 2024.

## Built on by (most cited first, 1 of 1)

- [Patient-Zero: Scaling Synthetic Patient Agents to Real-World Distributions without Real Patient Data](https://mlsanity.com/paper/2509.11078/) (2025)

## Abstract (arXiv)

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.