Modeling, Analyzing and Managing Insurance Risks

保险业务的本质是保险人承诺支付所有承保的索赔，以换取保单保费。此外，保险公司还会投入一些资金，以确保即使在灾难性事件或金融危机等特殊情况下也能兑现承诺。为了使保险公司能够有足够的资金来支付其责任，重要的是建立一个适当的模型，可以准确地预测未来的索赔金额，从而可以相应地设定保费和准备金。近年来，人们更加注意在考虑实际情况的同时对保险风险进行建模和分析，特别是考虑到时间和空间上的某些不同质性和依赖性。与此同时，许多现代保险产品也被设计和推出，以满足市场需求，如网络责任保险和与健康奖励计划相结合的人寿保险。我提出的研究旨在为新保险产品或面临大数据新世界新挑战的传统保险业务的各种保险风险的随机和统计建模和分析提供新思路和创新方法。我还致力于研究与风险相关的数量/措施，这些措施可以被保险公司用来理解、预测和管理这些风险。该研究具有实际意义，因为大多数模型和分析方法都是由实际问题或数据驱动的（例如，网络保险风险模型、作物保险或灾难性损失的具有空间依赖性的可信度模型）。我提出的研究与最近业界和精算科学协会对预测分析的关注和发展趋势相吻合。大数据在开发有助于保护保险业务的预测模型方面发挥着重要作用。对于精算师来说，在大型数据集上使用预测建模和数据分析技术来发现未来的模式也很重要。预期结果将为预测分析真实保险索赔数据和解决对保险从业者至关重要的新保险挑战提供有用的建模方法。例如，随机建模网络风险可以帮助保险公司更准确地评估保险相关损失，使他们能够更好地管理他们的产品并提供公平的定价。它也可以帮助高科技和专业服务公司更好地了解网络保险产品如何提供保护，防止网络攻击/事件造成的经济损失。将人寿保险与健康奖励计划结合起来的研究，可以帮助保险公司更有信心地推广他们的新产品，同时提供适当的折扣来激励投保人的健康行为和活动。

The nature of insurance business is the promise by the insurer to pay all covered claims in exchange for a policy premium. In addition, the insurer commits some capital to assure that the promise will be kept even under special circumstances such as catastrophic events or financial crises. In order for the insurer to be able to have enough funds prepared to pay its liabilities, it is important to build an appropriate model that can predict future claim amounts accurately, so premiums and reserves can be set accordingly. In recent years, more attention has been given to modeling and analyzing insurance risks while taking practical considerations, to allow particularly for certain inhomogeneity and dependence in time and space, into account. Meanwhile, many modern insurance products have been designed and launched to meet the market needs such as cyber liability insurance and life insurance integrated with a health rewards program. My proposed research aims to provide new ideas and innovative approaches for stochastically and statistically modeling and analyzing various insurance risks associated with new insurance products or traditional insurance businesses facing new challenges due to the new world of big data. I also aim to study risk-related quantities/measures that could be used by insurers to understand, predict and manage these risks. The proposed research is of practical importance because most models and analytical approaches are driven by real problems or data (e.g., models for cyber insurance risks, credibility models with spatial dependence for crop insurance or catastrophic losses). My proposed research coincides with the recent focus and development trend on predictive analytics in industry and within the actuarial science societies. Big data plays an important role in developing predictive models that help to protect insurance businesses. It is also important for actuaries to discover future patterns using predictive modeling and data analysis techniques on large data sets. The anticipated outcomes will provide useful modeling approaches for predictively analyzing real insurance claims data and addressing new insurance challenges that may be vital to insurance practitioners. For example, stochastically modeling cyber risks may help insurance companies in assessing insurance related losses more accurately, enabling them to better manage their products and offer fair pricing. It may also help high-tech and professional service firms to better understand how cyber insurance products provide protection against economic losses caused by cyber attacks/incidents. The study of integrating life insurance with health rewards programs may help insurers promote their new products with confidence while offering appropriate discounts to motivate/incentivize policyholders' health behaviour and activities.