Risk Management & Insurance Review
Volume 23, Issue 4
By
Xian Xu, Fudan University, Shanghai, China and Peter Zweifel, University of Zurich, Bad Bleiberg, Austria
In recent years, the insurance industry has known rapid development and application of new technologies, leading to the emergence of a large number of innovative products. This constitutes a challenge for stakeholders ranging from consumers, management, investors, and on to regulators, who need to evaluate these so‐called InsurTech innovations. This study applies a modified Delphi method in combination with the Analytical Hierarchy Process of Saaty evaluate potential innovations on three main dimensions, (i) management and operations, (ii) level of technology, and (iii) user experience. The authors propose a transparent way of evaluating InsurTech innovations that also may provide guidance for their future development.
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Risk Management & Insurance Review
Volume 23, Issue 4
By
Robert W. Klein, Temple University, Philadelphia, PA, USA and Harold Westin, Georgia State University, Atlanta, GA, USA
Many businesses have suffered severe economic losses due to the COVID‐19 pandemic. Because property business interruption (BI) policies generally do not cover losses caused by a virus, this has led to proposals for some form of government program that would provide this coverage. This paper explains why private BI pandemic insurance on a broad scale is infeasible. It considers the goals of a government BI pandemic insurance program and the challenges it would face with respect to its design and implementation and how they could be addressed and concludes that creating such a program requires thorough and careful consideration of its features and the tradeoffs involved with its structure.
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Journal of Risk and Insurance
Early View
By
Keith K. Crocker, Risk Management Department, Pennsylvania State University, University Park, Pennsylvania and Nan Zhu, Risk Management Department, Pennsylvania State University, University Park, Pennsylvania
It has been established that categorical discrimination based on observable characteristics such as gender, age, or ethnicity enhances efficiency. We consider a different form of risk classification when there exists a costless yet imperfectly informative test of risk type, with the test outcome unknown to the agents ex ante. We show that a voluntary risk classification in which agents are given the option to take the test always increases efficiency compared with no risk classification. Moreover, voluntary risk classification also Pareto dominates a regime of compulsory risk classification in which all agents are required to take the test.
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