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English(EN) Discrimination-free Insurance Pricing with Privatized Sensitive Attributes

新研究探索具有可调和隐私保护模型的保险定价公平性

两篇研究论文探讨了保险定价公平性的新方法,解决了精算公平性和团结公平性之间的张力。第一篇论文介绍了一个“alpha-公平个体偿付能力保费”($\alpha$-FISP)框架,该框架允许在精算公平定价和基于团结的定价之间进行可调的连续性,同时确保偿付能力。第二篇论文侧重于无歧视定价,通过使用私有化敏感属性,即使由于隐私或监管原因限制直接访问性别或种族等敏感数据,也能实现公平定价。 AI

影响 这些论文引入了新的保险定价算法框架,在公平性和偿付能力之间取得平衡,可能影响未来的精算实践和监管方法。

排序理由 该集群包含两篇在arXiv上发表的学术论文,详细介绍了保险定价的新方法。

在 arXiv cs.LG 阅读 →

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Tianhe Zhang, Xiguang Liu, Peng Shi ·

    {\alpha}-Fair Insurance Pricing: A Fairness Continuum

    arXiv:2606.14898v1 Announce Type: new Abstract: Fairness in insurance pricing remains a long-standing and deeply debated puzzle. On one hand, insurers, driven by profitability considerations, set premiums that differentiate across individual risks to achieve actuarial fairness. O…

  2. arXiv cs.LG TIER_1 English(EN) · Tianhe Zhang, Suhan Liu, Peng Shi ·

    Discrimination-free Insurance Pricing with Privatized Sensitive Attributes

    arXiv:2504.11775v3 Announce Type: replace-cross Abstract: Fairness has become an important concern in insurance pricing as insurers increasingly rely on machine learning models to predict expected losses. At the same time, regulatory and privacy constraints often restrict insurer…