基于潜在马尔可夫风险模型的频率-严重度经验评级
Frequency-Severity Experience Rating based on Latent Markovian Risk Profiles
摘要：
传统上，奖惩系统会考虑客户的索赔数量，而不考虑索赔的大小，即使这些组件在实践中是相互依赖的。我们提出了一种新的联合经验评级方法的基础上潜在的马尔可夫风险配置文件，允许一个积极的或消极的个人频率严重程度的依赖。隐马尔可夫模型中的潜在配置文件随着时间的推移而演变，以捕获客户索赔体验的更新，使索赔计数和大小有条件地独立。我们表明，由此产生的风险溢价导致一个动态的，索赔经验加权混合标准的可信度溢价。所提出的方法被应用到荷兰汽车保险组合，并确定客户的风险配置文件与独特的索赔行为。反过来，这些特征使我们能够更好地区分客户风险。
英文摘要：
Bonus-Malus Systems traditionally consider a customer's number of claims irrespective of their sizes, even though these components are dependent in practice. We propose a novel joint experience rating approach based on latent Markovian risk profiles to allow for a positive or negative individual frequency-severity dependence. The latent profiles evolve over time in a Hidden Markov Model to capture updates in a customer's claims experience, making claim counts and sizes conditionally independent. We show that the resulting risk premia lead to a dynamic, claims experience-weighted mixture of standard credibility premia. The proposed approach is applied to a Dutch automobile insurance portfolio and identifies customer risk profiles with distinctive claiming behavior. These profiles, in turn, enable us to better distinguish between customer risks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
