NAIC Price Flashcards
Price optimization vs. traditional ratemaking techniques
Price optimization is a process that uses:
* big data (data mining of insurance & non-insurance personal info where permitted by law)
* advanced statistical modeling
* makes granular adjustments to indicated rates (specific risk classifications, or even individuals)
4 principles of ratemaking
- A rate is an estimate of the expected value of future costs
- A rate provides for all costs associated with the transfer of risk
- A rate provides for the costs associated with an individual risk transfer
- A rate is reasonable and not excessive, inadequate, or unfairly discriminatory if it is an actuarially sound estimate of the expected value of all future costs associated with an individual risk transfer
Define price optimization
- process of maximizing or minimizing a business metric
- uses sophisticated tools and models to quantify business considerations
Cost-based rate
traditional actuarially derived rate based on loss costs, LAE, and other expenses
Price elasticity of demand
Change in quantity demanded versus the price
- high elasticity: consumers will shop around even if prices only go up a little
- low elasticity: price doesn’t have much effect on demand
Define ratebook optimization, individual price optimization, hybrid optimization
ratebook optimization: adjust factors in a cost-based rating structure using a demand model
individual price optimization: build a pricing structure based on both cost and demand
hybrid optimization: insert a new rate factor based on demand (into an existing rate cost-based structure)
Main differences between traditional ratemaking and price optimization
traditional: applied at class level
price optimized: can be applied to individual policies
traditional: uses cost-based pricing
price optimized: incorporates non-cost-based considerations like propensity to shop around
traditional: deviations from indicated rates are subjective
price optimized: deviations from indicated rates are based on quantitative models
traditional: will assign same price to identical risks
price optimized: may assign different prices
traditional: generally accepted by regulators
price optimized: may not be acceptable
Benefits of Price Optimization
- doesn’t unfairly discriminate (low-income customers are more likely to shop around and not be penalized by non-cost-based increases)
- provides more accurate pricing (neither inadequate nor excessive)
- if optimization is applied on a ratebook level, it is not unfairly discriminatory
- note that individual optimization may be unfairly discriminatory
Drawbacks of Price Optimization
- regulators don’t have the data to independently verify rates based on price optimization
- the models (often GLMs) can produce large individual rate swings (can be controlled by using constrained optimization however)
- no evidence of improved stability from using price optimization (effect on long-terms costs is inconclusive)
- concern that ratemaking ASOPs may be violated
Possible regulatory responses to price optimization rating plans
- determine permissibility with respect to state laws
- define regulatory constraints (min/max rate swings, methods apply only to rate classes of at least a certain size)
- transparency: require full explanation of:
1. DAM - data, methods, assumptions
2. rate differences between customers with identical risk profiles
Disclosures a regulator may require when price optimization is used in a rate filing
- rate adjustments that are not cost-based
- whether price optimization was used
- which rating factors are affected by price optimization and their quantitative impact
- whether customers with the same risk profile have different rates
- data sources and models that affected the rate charged in any way
Recommendations of the Task Force on Price Optimization regarding pricing methodology
- rates should be cost-based
- rates should comply with state law
- customers with identical risk profiles should be charged the same rate
Rating considerations that the Task Force on Price Optimization believes are unfairly discriminatory
- price elasticity of demand
- propensity to shop for insurance
- retention adjustment at an individual level
- a policyholder’s propensity to ask questions or file complaints
Recommendations of the Task Force on Price Optimization regarding state regulatory practices
- issue bulletin addressing use of non-cost-based methods
- enhance disclosure requirements for state filings
- ensure compliance with state laws and actuarial principles by analyzing insurers’ rating models
