Summary of Zero-inflated Tweedie Boosted Trees with Catboost For Insurance Loss Analytics, by Banghee So and Emiliano A. Valdez
Zero-Inflated Tweedie Boosted Trees with CatBoost for Insurance Loss Analytics
by Banghee So, Emiliano A. Valdez
First submitted to arxiv on: 23 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 The paper proposes advanced modifications to the Tweedie regression model to address its limitations in modeling aggregate claims for various types of insurance. The traditional Tweedie models are effective but struggle to accurately represent the large incidence of zero claims. To overcome this, the authors recommend a refined modeling of the zero-claim process and integrate boosting methods to enhance predictive accuracy. They choose CatBoost as their efficient boosting approach that handles categorical data. The paper demonstrates the efficacy of the enhanced Tweedie model using an insurance telematics dataset, showcasing improved model performance suitable for insurance claim analytics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper makes the Tweedie regression model better by fixing its problem with zero claims. Zero claims are common in car, health, and liability insurance, but traditional models don’t get it right. The authors suggest a new way to model zero claims and use boosting methods to make predictions more accurate. They choose CatBoost because it works well with special types of data. They test their idea on an insurance dataset and show that it’s better than the old method. |
Keywords
» Artificial intelligence » Boosting » Regression
