Summary of Reinforcement Learning Applied to Insurance Portfolio Pursuit, by Edward James Young et al.
Reinforcement Learning applied to Insurance Portfolio Pursuit
by Edward James Young, Alistair Rogers, Elliott Tong, James Jordon
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); 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 presents a novel reinforcement learning algorithm for solving the portfolio pursuit problem in insurance firms. The authors formulate the problem as a sequential decision-making challenge, where an insurer must consider multiple factors when deciding on an offer to make to a new customer. These factors include expected cost of providing the insurance, competitor offers, and customer sensitivity to price differences. The paper proposes a novel algorithm for solving this problem, which is tested in a complex synthetic market environment. Results show that the proposed method outperforms a baseline approach, which mimics current industry practices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Insurance companies need to decide what offer to make to new customers, considering many factors like costs and competitor offers. They often target specific groups of customers based on characteristics like age, location, or occupation. The authors call this problem “portfolio pursuit” and propose a new way to solve it using reinforcement learning. This method is tested in a simulated market and shows better results than current industry practices. |
Keywords
* Artificial intelligence * Reinforcement learning
