Summary of Generative Ai For Banks: Benchmarks and Algorithms For Synthetic Financial Transaction Data, by Fabian Sven Karst et al.
Generative AI for Banks: Benchmarks and Algorithms for Synthetic Financial Transaction Data
by Fabian Sven Karst, Sook-Yee Chong, Abigail A. Antenor, Enyu Lin, Mahei Manhai Li, Jan Marco Leimeister
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study explores the use of generative AI to overcome challenges in using deep learning in the banking sector due to data sensitivity and regulatory constraints. It evaluates five leading algorithms – CTGAN, DGAN, Wasserstein GAN, FinDiff, and TVAE – across five criteria: fidelity, synthesis quality, efficiency, privacy, and graph structure. While no algorithm replicates real data’s graph structure, each excels in specific areas. For instance, DGAN is suitable for privacy-sensitive tasks, while FinDiff and TVAE excel in data replication and augmentation. CTGAN achieves a balance across all five criteria, making it suitable for general applications with moderate privacy concerns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study uses artificial intelligence to help banks use machine learning without sharing their private information. It compares different AI models to see which one works best for generating fake financial data that’s similar to real data. Each model is good at something specific: some are better at keeping the data private, while others are better at making fake data that looks like real data. The best model is called CTGAN and it does a little bit of everything well, so it’s good for most tasks. |
Keywords
» Artificial intelligence » Deep learning » Gan » Machine learning
