Summary of Utilizing Gans For Fraud Detection: Model Training with Synthetic Transaction Data, by Mengran Zhu et al.
Utilizing GANs for Fraud Detection: Model Training with Synthetic Transaction Data
by Mengran Zhu, Yulu Gong, Yafei Xiang, Hanyi Yu, Shuning Huo
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the use of Generative Adversarial Networks (GANs) in fraud detection, comparing their effectiveness with traditional methods. The authors argue that GANs’ ability to model complex data distributions makes them a promising tool for anomaly detection. The study systematically describes the principles of GANs and their derivative models, highlighting their application in fraud detection across various datasets. The authors aim to design and implement a fake face verification code and fraud detection system based on GANs algorithm to enhance transaction security. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to stop fake transactions from happening. It uses special computer networks called Generative Adversarial Networks (GANs) that are good at finding things that don’t fit normal patterns. The authors want to see if using GANs can help keep transactions safe by catching fake ones before they happen. |
Keywords
* Artificial intelligence * Anomaly detection
