Insurance Industry Shifts: Analytics, Pandemics & Trade Credit

Introduction: Forces Reshaping the Insurance Industry

Data analytics, cyber capabilities, climate change, and the most recent pandemic have fundamentally altered the way insurance is disseminated around the world. The uncertainty surrounding insurance clauses such as force majeure has caused insurance companies to incur higher costs related to business disruptions and legal expenses. Likewise, the widespread nature of claims has, in certain instances, threatened the solvency of insurance companies that did not adequately model large-scale business disruptions. As a result, there has been a fundamental shift in the way management engages with insurance companies, how insurance companies retain clients, and how risk is effectively transferred to avoid solvency issues.

COVID-19 and the Redefinition of Insured Perils

The recent COVID-19 pandemic has reshaped many definitions related to insured perils. This is heavily related to insurance contracts that often did not include epidemics or pandemics within their terms. As a result, insurance contracts now include specific language addressing these perils and how they apply to the force majeure standard. This is particularly important for insurance related to small businesses and "mom and pop" operations, which often have very low working capital buffers to sustain a prolonged business disruption. Managers of these operations are therefore seeking coverage that clearly provides indemnification for unforeseen circumstances related to pandemics or epidemics. This will be a trend in future insurance contracts, which will look to further clarify definitions, establish specific claim limits, and eliminate obscure language (Dercon, 2003).

Data Analytics and Its Impact on Insurance Operations

Data analytics has also significantly impacted the overall insurance industry. When utilized properly, data analytics substantially reduces the loss ratio for insurance companies. With lower losses, insurance companies can increase profitability by maintaining relatively modest returns on their float and invested premium. Companies such as Progressive and Geico have made heavy use of data analytics to further segment their insured populations. This allows them to more accurately charge higher premiums to higher-risk policyholders, better compensating the organization for the hazards it is underwriting. This has shaped the industry by helping to lower loss ratios, charge adequately for the risk being assumed, and improve overall business profitability.

This dynamic is also important as it relates to the transfer of risk to other insurers. Reinsurance, for example, has become much more prominent in the wake of the pandemic. Due in part to COVID-19, reinsurance rates continue to rise to compensate insurers for the uncertainty surrounding prolonged business disruptions. Data analytics has been a cornerstone of this pricing, as it provides a large number of data points that can be used to determine the "tail risk" associated with an insured peril. Tail risk is generally defined as the likelihood and magnitude of an extremely unlikely event occurring. Data analytics helps management better understand the implications of a rare event — such as a pandemic — and how it can impact the overall operations of a business. Due in part to these models, reinsurance rates continue to rise to better compensate insurers for the risks they assume.

The application of data analytics also enables much more efficient service for policyholders experiencing a loss. Technology has allowed insurance providers to limit fraudulent claims, provide faster claims resolution, and ultimately serve society more efficiently.