Data analytics, cyber capabilities, climate change, and the most recent pandemic have fundamentally altered the way insurance is disseminated around the world. For one, the uncertainty surrounding insurance clauses such as Force Majeure, have caused insurance companies to pay higher costs related to business disruptions and legal expenses. Likewise, the widespread nature of claims, in certain instances, threaten the solvency of insurance companies who did not appropriate model large business disruptions throughout the world. As a result, there is fundamental shift in the way management engages with insurance companies, how insurances companies retain clients, and how risk is effectively transferred to avoid solvency issues.
To begin, the recent COVID-19 pandemic has reshaped many definitions related to insured perils. This is heavily related to insurance contracts who often did not include epidemics or pandemics within the contract. As a result, insurance contracts now include specific language related to these perils and how they will apply to the force majeure standard. This is particularly important for insurance related to small businesses or other Mom and Pop operations which often have very low working capital buffers to support a prolonged business disruption. Therefore, managers of these operations are looking to pay for coverage that clearly provides indemnification for unforeseen circumstances related to pandemics or epidemics. This will be a trend of future insurance contracts which will look to further clarify definitions, provide specific claim limits, and eliminate obscure language (Dercon, 2003).
Data analytics has also significantly impacted the overall insurance industry. Data analytics, when utilized properly, heavily reduces the loss ratio for insurance companies. With lower losses, insurances companies can increase profitability by maintaining relatively modest returns on their float and invested premium. Companies such as Progressive and Geico have heavily utilize data analytics...
References
1. Dercon, S., & Krishnan, P. (2003). Risk Sharing and Public Transfers. The Economic Journal, 113(486), C86–C94. http://www.jstor.org/stable/3590049
2. Houston, D. B. (2014). Risk, Insurance, and Sampling. The Journal of Risk and Insurance, 31(4), 511–538. https://doi.org/10.2307/250806
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