Many successful insurance companies offer the best rates to retain low-risk customers while pricing adequately for higher risk. Those that struggle to maintain this balance could see their portfolios suffer from adverse selection and attrition. TransUnion conducted several analyses with insurance providers to examine how credit information can help address this issue. These analyses help provide a clearer view of consumer risk to assist insurers with pricing appropriately and improving the performance of their portfolio over time. Laurent Holleville, Advisor at TransUnion, explains how.
Pricing for price-sensitive consumers
Insurance coverage is something most of us can’t do without, but insurance premiums aren’t an expense we enjoy incurring. The good news for consumers is that it is easier than ever to shop around for the best rates and switch insurance providers for a better deal.
In this market, insurers need to offer competitive rates to attract customers while maintaining adequate reserves to ensure they can meet their obligations: a balancing act that’s made more complex by the fact that insurance is not a one-price-fits-all product.
If you’re not able to price premiums adequately for high-risk clients, you’re potentially losing profit — and making your products more attractive to high-risk customers that other insurers may not take on. Because higher losses can put pressure on pricing, at some point, you may have to raise your rates, and lower-risk customers are likely to leave.
To break this cycle, you need greater insight into the relative risk of the consumers applying for insurance as well as the customers already on your book.
The predictive power of credit data
Credit data adds more predictive power to the traditional rating algorithms used by insurance companies and can help improve the overall performance of their portfolios.
In collaboration with insurance providers, TransUnion analyzed a population of consumers, over a period of seven years, in a property insurance portfolio not yet using credit data for pricing. We applied our Property Insurance Score, built using credit data, which can help predict the likelihood of an insurance loss on a property portfolio.
Using the outputs of the Property Insurance Score, we ranked ordered the population into ten groups of equal sizes. Individuals with lowest scores were placed in group 1, and segments of increasingly higher scores were put in groups 2 through 9, with the individuals with the highest scores placed in group 10. We then looked at the insurers’ loss ratios across all ten groups to see how this aligned with their risk profiles.
As we see in Figure 1, credit information can add additional predictive information not captured by traditional rating variables. When applying the Property Insurance Score, TransUnion identified customer segments with loss ratios of over 100% (which means the premiums aren’t covering losses) to around 50% (premiums are too high, and the business may lose customers to other insurers).
Figure 1: Graph showing the loss ratio of clients scored from 1 (high risk) to 10 (low risk) in property insurance portfolios
Credit-based pricing benefits
An additional analysis, using credit data on a portfolio not yet using credit for pricing, revealed consistent trends that pointed to adverse selection. Over a 7-year period, it appeared that new insurance policies were issued to individuals with consistently lower credit scores, and individuals who were more inclined to cancel their policies had higher credit scores, with their average score increasing over time. Our analysis showed further potential for a credit-based pricing strategy to assist with reversing some of these trends and help to reduce the risk of the portfolio.
For example, we found that over 30% of the portfolio appeared to be underpriced and approximately 10% of customers may need a 100% premium increase to cover potential insurance claims. Additionally, about 50% of customers were at risk of attrition and could be offered lower rates to help prevent them from moving their business elsewhere.
Insurers that don’t use credit data to analyze their portfolio may well see some of their best customers leaving, while new business becomes riskier. Introducing credit data to your insurance business can have a beneficial impact on your portfolio, as this data can help you identify where premium rates can be adjusted — helping competitiveness in the long term.