The total life insurance coverage in the U.S. was $19.2 trillion in 2011, with $2.9 trillion of new life insurance purchased that year. However, nearly 6.1 percent of the face amount and number of policies, or roughly $1.1 trillion, is terminated every year.
Policyholders either lapse on their premiums or surrender their policies outright, causing a significant revenue drain for insurers. Understanding past policyholder behavior and making assumptions about how current and future policyholders are likely to behave in the future are critical to managing the business for insurers.
Policyholder behavior in terms of purchase behavior, withdrawal behavior, surrender or lapse behavior and option exercise behavior are all essential in determining several actions. These include how to market insurance products, price products and evaluate product profitability, compensate agents and advisors for acquisition and retention of policyholders, value assets, liabilities, reserve and capital for various economic conditions as well as transfer or hedge the risks.
Insurance professionals have used a number of mathematical, statistical, financial and economic theories to understand policyholder behavior and quantify future liabilities and risks. Assumptions about future policyholder behavior form a key aspect of insurers’ pricing, reserving and hedging strategies and policies. Earlier attempts at modeling policyholder behavior have taken deterministic or stochastic approaches of modeling the base and dynamic behavior of policyholders. Such approaches suffer from two major drawbacks:
- Aggregate Level Modeling: The approaches have been at an aggregate level with little or no differentiation of policyholder behavior based on different socio-demographic, attitudinal or behavioral factors. This aggregate level analysis fails to account for the value that different policyholders place on certain features, such as the number and type of fund choices available within a life insurance policy or annuity contract, liquidity versus guarantees.
- Rational Approach: The approaches have assumed a classical rational expectations approach and do not account for how strongly social, cognitive and emotional factors influence consumers’ financial decisions. For example, policyholder decisions around lapses or surrender may not be based on in-the-moneyness (ITM) of an option but may be driven by loss aversion, job insecurity and the need for liquidity.
Recently, insurance professionals have begun to address these two issues by embracing behavioral economics and predictive modeling.
Behavioral economics is the study of actual (as opposed to rational) decision making by consumers and takes into account their social, cognitive and emotional biases. In addition, behavioral economics provides insights into changing policyholder behaviors by nudging policyholders to make decisions that are beneficial to them and the system overall.
In-depth analyses have uncovered the underlying behavioral principles, such as bounded rationality and willpower that are driving decision making. For example, risk-averse consumers should place a higher value on annuities with minimum guarantees that provide income for life because they offer protection against longevity and equity risk. However, it is well known that pre-retirees and retirees fail to annuitize any lump-sum savings, either in full or partially. This is often referred to as the annuity puzzle.