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

Fabian Sven Karst, Sook-Yee Chong, Abigail A. Antenor, Enyu Lin, Mahei Manhai Li +1 more
12/19/2024
cs.LG

No official code found. Best implementation: Faruman/Comparison_FinancialDataGeneration (6 stars, last commit 2025-03-20).

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.

View on arXivView PDF

Code Implementations(1)

Faruman/Comparison_FinancialDataGenerationCommunity100%

The following repository contains the online appendix for the paper "Generative AI for Banks: Benchmarks and Algorithms for Synthetic Financial Transaction Data". It not only provides the python code for all experiments conducted but also background information for the literature review.

60PythonJul 8, 20241 years ago
ctgandoppelgangerfindiffsynthetic-datatvae+1 more

Similar Papers

Synthetic Demographic Data Generation for Card Fraud Detection Using GANs

85% match1 repo52023

PersonaLedger: Generating Realistic Financial Transactions with Persona Conditioned LLMs and Rule Grounded Feedback

83% match2026

Detection of AI Deepfake and Fraud in Online Payments Using GAN-Based Models

83% match2025

Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Review

81% match2025

Realistic Synthetic Financial Transactions for Anti-Money Laundering Models

81% match1 repo1152023

AI versus AI in Financial Crimes and Detection: GenAI Crime Waves to Co-Evolutionary AI

81% match2024

Cite this paper

@article{karst2024generative,
  title  = {Generative AI for Banks: Benchmarks and Algorithms for Synthetic Financial Transaction Data},
  author = {Fabian Sven Karst and Sook-Yee Chong and Abigail A. Antenor and Enyu Lin and Mahei Manhai Li and Jan Marco Leimeister},
  year   = {2024},
  eprint = {2412.14730},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2412.14730v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2412.14730.svg)](https://codeofpaper.com/paper/2412.14730)

Discussion