Using agent-based modeling to understand policyholder behaviors

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.

Predictive Modeling: According to the SOA Predictive Modeling Survey Subcommittee, upwards of 40 percent of survey respondents are using or considering using predictive modeling to better understand policyholder behavior. Predictive modeling uses statistical techniques to understand the interactions between many factors that influence a policyholder's decisions.

For example, predictive modeling can help insurers determine the interaction between income and age, and the impact it has on lapse rates. This is more powerful than traditional techniques that commonly account for very few variables when modeling policyholder behavior, and do not typically account for the interaction effects of those variables.

While significant advances are being made in the use of behavioral economics and predictive modeling in understanding policyholder behavior, there are two fundamental challenges:

Modeling individual policyholder behaviors: Behavioral economics describes a number of shortcuts or decision rules that people use when making decisions under limited and uncertain information. These decision rules, or use of defaults, hyperbolic discounting and endowment principle, are often used to explain policyholder decisions discussed earlier. However, using these decision rules to consistently model and evaluate impact on insurer assets and liabilities requires us to move away from an aggregate level model to an individual consumer or policyholder-level model.
Modeling causal structure of individual decision making: While predictive modeling is more effective than traditional techniques in capturing the interaction between multiple variables, it fails to capture the rich structure of causal influences and non-quantitative factors, such as the emotional and social factors that influence policyholder decision making. Furthermore, predictive modeling relies on historical experience to predictive future experience.

Thus, it is not very reliable predicting future experience when there is a fundamental change in the environment. Individual software agent-based models, extensively used in artificial intelligence based systems, can effectively capture the complex causal structure of individual policyholder decision making under diverse environmental conditions.

Behavioral simulation combines individual decision rules and AI-based software agent modeling to model policyholder behavior. Advances in artificial intelligence allow us to simulate behavior at an individual level and then analyze the aggregate outcomes. These models simulate the simultaneous operations and interactions of multiple individuals to recreate a system and predict complex phenomena. This process results in emergent behavior at the macro level based on micro-level system interactions.

The concept is that the simple behavioral rules that define the simulated individuals' actions generate complex behavior at the macro level. The behavioral rules for each individual are based on the segment-specific behavioral economic principles informed by the consumer data.

This approach is applicable for modeling a variety of purchasing, withdrawal, lapse or surrender and option exercise behaviors. Simulation models are beginning to play a central role in the design, distribution and risk management of insurance products. They promote a more sophisticated understanding and evaluation of product design, pricing, valuation, reserving and hedging.

Using agent-based modeling to understand policyholder behaviors

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