Data analytics risk transfer

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 to further segment their insured population. Here, they are much better able to charge higher premiums to those at risk to better compensate the organization for the hazards they are insuring. This has shaped the industry by helping to lower loss ratios, charge adequately for the risk being taken, and to improve overall business profitability. This assessment is important as it relates to the transfer of risk to other insurers as well. For example, reinsurance is now becoming much more prominent due to the pandemic. Due in part to the COVID-19 pandemic, reinsurance rates continue to rise to compensate insures for the uncertainty surrounding a prolonged business disruption. Data analytics has been a cornerstone of this pricing as it provides a large amount of data points which can be used to determine the “tail risk” associated with an insured peril. Tail risk is often defined as the likelihood and magnitude of an extremely unlikely event occurring. Data analytics helps management better understand the implications of an extremely rare event, such as a pandemic, and how it can impact the overall operations of the business. Due in part to these models, reinsurance rates continue to rise to better compensate insures for the risks they take. The application of data analytics also serves to also provide much more efficient service to those experiencing a loss. Technology, for example, has enabled insurance providers to limit fraudulent claims, provide faster claims resolution, and ultimately serve society more efficiently.

Due in part to the factors mentioned above, the industry appears to be headed into an era in which data analytics will more readily inform management decision making. We are also heading into an era where uncertain terms such as “Force Majeure” will be more readily defined within the loan documents. In addition, there will be legal implication on the part of the pandemic as intermediaries better determine who should be indemnify the insured for confusing contractual terms.

Trade credits insurance is a risk management tool used to help protect businesses from bad or uncollectable debts. Essentially, the primary components of trade credit insurance are related to the amount of accounts receivable owed per customer, the credit worthiness of those customers, the location of the customers, and the overall risk associated with the business the customer operates in. These components are critical as it allows insurance providers to properly ascertain the ability of the business to adequately repay the receivable over time. This insurance will typically protect the business from bankruptcy, default, or other perils agreed to within the contract. Businesses engage in this form of insurance to protect their cash flow and maintain adequate working capital to run the business in the event of a major customer default. This is ideal for small business, who typically have one or two major customers. As such, a default, could endanger the overall continuing operations of the business. Here, government involvement helps the market to the extent that capital dries up or insures are unwilling underwrite policies. Here government intervention can ensure consumer confidence, maintain the integrity of the market, and provide an emergency backstop in the event of many defaults within the business environment. This maintains the market as insures are more confident that the government can intervene on their behalf. Likewise, the insured have assurances that the contracts will be maintain and upheld (Houston, 2014).