Summary of Credit Card Fraud Detection: a Deep Learning Approach, by Sourav Verma et al.
Credit Card Fraud Detection: A Deep Learning Approach
by Sourav Verma, Joydip Dhar
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 In this paper, researchers tackle the significant issue of fraudulent credit card transactions, which have resulted in substantial financial losses for institutions and individuals. Current AI-based fraud detection systems rely on assumptions that do not effectively address the challenges of concept drift, class imbalance, and verification latency. To mitigate these issues, the authors propose implementing Deep Learning algorithms to achieve high fraud coverage with low false positive rates. Specifically, they aim to develop an auto-encoder as a semi-supervised method for learning common patterns in fraudulent transactions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper aims to improve credit card fraud detection by developing a more effective algorithm using Deep Learning. The current systems are not doing well enough to catch the increasing number of fraudulent transactions. This new system will be able to identify and stop these types of crimes, reducing financial losses for everyone involved. |
Keywords
» Artificial intelligence » Deep learning » Encoder » Semi supervised
