When Insurance Becomes a Risk Factor

If compound interest is humanity’s greatest invention, insurance may be a close contender.

Today, activities we take for granted – such as securing a home loan – would be significantly more expensive without well-developed insurance markets. But what happens when insurance itself becomes a risk factor?

Let’s now consider the steps that risk managers can take to address the challenges posed by the perverse incentives insurance can create and the risks that can arise when insurance is suddenly no longer available.

By Cristian deRitis

Reviewing the Classics

By pooling their resources and risks together, early humans were able to increase the chances that our species would survive. Indeed, throughout history, insurance contracts have facilitated risky endeavors, such as trade and innovation, by enabling entrepreneurs to take necessary risks.

But insurance products also present obstacles. Any seasoned risk management professional will be familiar with the terms “moral hazard” and “adverse selection,” which summarize key challenges to the insurance industry. A quick recap of their meaning is helpful, as we consider ways to minimize the risks that can arise with insurance.

Moral hazard describes the situation where one party in a transaction takes on additional risks that negatively affect the other. A classic example is a homeowner who might neglect safeguarding their property against hazards like fire or flooding once they obtain insurance coverage, leading to a higher likelihood of loss than an insurer might have anticipated. This misalignment can result in insurance premiums that fail to cover expected losses.

Adverse selection, on the other hand, emerges under conditions of asymmetric information. In the insurance context, a business or individual seeking insurance may privately know that he/she/they are riskier than what publicly observable data suggests.

For example, a driver with no reported accidents and an excellent credit score may know that they engage in high-risk behaviors such as speeding. Keeping such discrepancies in mind, insurers might seek to increase overall premiums to compensate for the higher losses resulting from these "hidden" risks. However, by doing so, an insurer might inadvertently end up with a riskier pool of customers, because only riskier drivers would be willing to accept the higher premium.

In both instances, an insurance contract intended to mitigate risk may end up distorting prices or inducing more risk-taking behavior. Consequently, insurance companies expend considerable time and effort to collect data on their customers and design contracts that properly align the incentives of the insured with those of the insurer.

An Emerging Insurance Risk

Besides moral hazard and adverse selection, market failures affecting insurance availability and cost are increasingly impacting businesses and households, as well as the financial institutions that serve them – including those that are not direct parties to insurance contracts.

For instance, in states like Florida and California, the rising frequency of natural disasters and higher construction costs are driving up insurance losses. Regulatory caps on premium increases have led some insurers to withdraw from these states entirely as, by their calculations, they cannot collect enough revenue to cover the potential outlays they face in a disaster.

State-sponsored "last resort" plans have filled the gap in insurance-deficient states, so far. However, over-relying on these private reinsurance plans is dangerous – particularly if such funding sources become scarce or expensive, potentially leaving high-risk areas uninsured.

This situation raises concerns for both existing and future development in high-risk areas. Homeowners and lenders might find themselves unexpectedly exposed to losses, while higher insurance premiums could strain household finances and increase default risks across lending sectors.

For example, homeowners may have taken out mortgages years ago under the presumption that insurance would always be available. But if that insurance were to become either too pricey or entirely unavailable, then suddenly mortgage lenders that had assumed their risk exposure was limited may find themselves exposed to potential losses.

In addition to the direct impact on mortgage lenders, higher insurance premiums may affect household finances in other ways. For example, credit card and personal loan lenders may experience increased default rates rise if customers prioritize paying their insurance bills over their other debts. The strain on consumer finances, moreover, could impact a wide range of retailers and small businesses – and could ultimately push the economy into recession, if demand drops and unemployment rises.

Steps for Risk Managers

To mitigate these threats, risk managers can take four proactive steps:

1. Review insurance contracts regularly to ensure coverage aligns with evolving risks and costs. As insurers’ risk assessments are updated, businesses may find that they are much more exposed to threats than they previously thought. Changes in contracts upon renewal may shift responsibilities or limit coverage, necessitating careful examination.

2. Diversify counterparty exposures. Although counterparty management has always been a central part of risk managers’ responsibilities, it is more important than ever that companies understand the financial conditions of their counterparties and the options they may – or may not – have if one or more of them experiences financial difficulty. As recent history has shown, even large institutions with solid financial statements are not immune from the consequences of exponential risk.

3. Conduct scenario analysis and stress tests to identify key vulnerabilities related to changes in insurance coverage. Once weak points are identified, risk teams can enhance organizational resilience by proactively seeking out ways to either eliminate or mitigate potential threats.

4. Explore alternative insurance contract structures to better align incentives. Long-term mortgage contracts require homeowners’ insurance, but nearly all insurance policies have a one-year term. Under this set-up, there are times (as we’ve pointed out) when insurance is no longer available or becomes too costly. This, in turn, significantly increases default risk.

That’s why it’s logical to explore multiyear insurance contracts, which offer greater certainty to borrowers and reduce the likelihood that a premium increase leads to a loan default. While a longer-term policy might increase the monthly premium initially, the certainty of locking in a rate for three or five years would be a reasonable trade for most homeowners.

Parting Thoughts

Insurance is an essential part of the risk management toolkit and integral to the global financial system. Well-functioning insurance markets enable the efficient transfer of risks from individual households and businesses to a broader set of institutions with the resources necessary to bear them.

Insurers provide a valuable service to individuals looking to manage their risk exposure. Moreover, they also send powerful pricing signals that encourage or discourage certain behaviors. These incentives subtly shift economic activity toward more productive areas, while discouraging investments in areas where expected losses exceed expected rewards.

Just like every other market, however, insurance markets are imperfect and subject to potential market failures related to information asymmetries, poorly designed incentives, and the unintended consequences of well-intended regulation. Risk managers need to be cognizant of these potential shortcomings when modeling potential outcomes and setting their policies.

Insurance has an amazing ability to mitigate and distribute risk when used properly. It has the power to fuel the growth, exploration and discoveries that benefit all actors in the economy.

At the same time, when they are designed poorly, insurance contracts can hide, distort and even increase risks. As risk professionals, we need to be aware of the shifting insurance landscape and adapt accordingly.

Cristian deRitis is the Deputy Chief Economist at Moody's Analytics. As the head of econometric model research and development, he specializes in the analysis of current and future economic conditions, consumer credit markets and housing. In addition to his published research, Cristian is a co-host on the popular Inside Economics Podcast. He can be reached at cristian.deritis@moodys.com.