Modeling, Analyzing and Managing Insurance Risks

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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.