Summary of Improving Insurance Catastrophic Data with Resampling and Gan Methods, by Norbert Dzadz and Maciej Romaniuk
Improving Insurance Catastrophic Data with Resampling and GAN Methods
by Norbert Dzadz, Maciej Romaniuk
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Applications (stat.AP); Computation (stat.CO); Machine Learning (stat.ML)
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 This paper proposes three methods to improve the quality of large datasets related to catastrophic events, which are crucial for insurers. The proposed methods utilize the bootstrap, bootknife, and GAN algorithms to enhance the precision of these datasets. The authors conduct numerical experiments and real-life data analysis to compare the performance of these approaches based on mean squared error (MSE) and mean absolute error (MAE). Additionally, the paper presents a direct algorithm for constructing fuzzy expert opinions regarding these outputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure the data used by insurance companies is really accurate. The authors suggest three ways to make this data better using special computer algorithms. They test these methods on fake and real data to see which one works best, measuring how well they do with mean squared error and mean absolute error. They also show a way to combine expert opinions about the results. |
Keywords
» Artificial intelligence » Gan » Mae » Mse » Precision
