Summary of Enhancing Data Quality Through Self-learning on Imbalanced Financial Risk Data, by Xu Sun et al.
Enhancing Data Quality through Self-learning on Imbalanced Financial Risk Data
by Xu Sun, Zixuan Qin, Shun Zhang, Yuexian Wang, Li Huang
First submitted to arxiv on: 15 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 A novel approach to enhancing financial risk prediction models is proposed in this study, which addresses the limitations of existing datasets. The scarcity and diversity of high-quality data hinder machine learning model performance, particularly in credit default prediction and fraud detection. To overcome these challenges, a straightforward technique called TriEnhance is introduced. This method involves generating synthetic samples tailored to the minority class, filtering using binary feedback, and self-learning with pseudo-labels. The efficacy of TriEnhance is demonstrated across six benchmark datasets, with a focus on improving minority class calibration. This study aims to develop more robust financial risk prediction systems by leveraging TriEnhance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make finance models better is being studied. Right now, models are not as good as they could be because the data used to train them is limited and uneven. In credit default prediction and fraud detection, it’s really important to find high-risk instances quickly, because they can have big economic consequences. The researchers in this study came up with a simple method called TriEnhance that helps make existing financial risk datasets better. They tested it on six different datasets and showed that it works well. |
Keywords
» Artificial intelligence » Machine learning
