AI summary · arXiv: 1907.05381v1 · econ.EM
Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches
The essentials
- The paper explores the application of dynamic pricing to insurance as an online revenue management problem.
- Two pricing models are developed: an adaptive Generalized Linear Model (GLM) and an adaptive Gaussian Process (GP) regression model.
- Both models balance exploration and exploitation to optimize long-term revenue from selling a new insurance product.
- Performance is measured in terms of regret, which refers to expected revenue loss caused by not using the optimal price.
- Maximum quasi-likelihood estimation (MQLE) is used to estimate unknown parameters in their adaptive GLM design.
- The authors show that if prices are chosen with suitably decreasing variability, MQLE parameters eventually exist and converge to correct values, implying that the sequence of chosen prices will also converge to optimal price.
- In their adaptive GP regression model, demand and claims are sampled from Gaussian Processes before selling prices are determined by upper confidence bound rule.
- The study represents one of the first works to consider dynamic pricing problems specifically within insurance and exploring Gaussian Process regression within insurance pricing.
- Overall, these initial findings suggest that online machine learning algorithms could be a fruitful area for future investigation and application within insurance.
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