Many insurance companies are focusing on retention as a major tool to combat reductions in premium. But when policyholder retention is in the limelight, there’s danger that risk management takes a back seat, resulting in deterioration of long-term financial performance.
Accurate information key
Some insurers identify and price risk more effectively than others and achieve better financial and risk management results. One fundamental risk management tenet is that accurate rating information is the key element in the underwriting process. As we’ve learned painfully from the mortgage industry, when consumers misrepresent facts and underwriters don’t validate information, financial performance suffers and risk increases. The same holds true in the property/casualty auto insurance industry.
Data integrity failures
Americans lead dynamic lifestyles, resulting in frequent changes to data used to rate their auto policies. Every hour, there are approximately 250 marriages, 125 divorces, 5,600 changes in residence, 7,100 job changes, 6,400 registration changes, 450 new driver’s licenses, 160 DUI violations, and 2,800 auto claims. Each of those could potentially change the risk for an auto policy. What was accurate at application may be inaccurate today. To avoid high premiums, personal auto applicants may misreport rating information, such as annual mileage, usage, garaging location, drivers in the household, and driving history.
Misreported information can have severe adverse consequences when actuaries use the incorrect data to develop their rating plans, resulting in weak risk differentiation and risk management failures.
Table 1: Effect of poor data quality and risk differentiation on book of business
A good rating plan and underwriting process should differentiate risk and correctly price the risk so that, on average, more money is collected than paid out. A company’s ability to differentiate risk — and its underwriting processes to measure risk — must be equal to or better than its competitors’ to avoid adverse selection.
Table 1 (above) shows a scenario in which there are three policies — A, B, and C — on a company’s books. Those three policies have different risk profiles costing $1,000, $1,200, and $1,400, respectively. However, because the company can’t differentiate risk, the price for each policy is $1,260. In time period 1, the company collects premiums of $3,780, incurs $3,600 in costs, delivers underwriting profits of $180, and has a combined ratio of 95.2 percent. If the company’s competitor is able to differentiate and price the risks better, then, over a period of time, the company will lose policies A and B to the competition. At the end of time period 3, only policy C will remain on the books, with a premium of $1,300, a cost of $1,400, and an underwriting loss of $100. The competitor will have policies A and B, with premiums of $2,310, costs of $2,200, and an underwriting profit of $110.
Clearly, inaccurate data results in bad pricing decisions. That, in turn, leads to inadequate risk differentiation and risk management.
Retention as a tool to protect the top line without consideration to profitability is a losing proposition. Consider a company that has 100 policies and charges $1,000 for each policy. Assume that its combined ratio is 100: It collects $100,000 in premium, and total costs are $100,000. The company’s baseline renewal rate is 85 percent (a reasonable percentage for personal auto). Assuming no other changes, the company will collect $85,000 in premium at renewal. Expected costs are $85,000; underwriting profit is still zero.
Through a systematic reunderwriting program, the insurer identifies one of the 85 policies as misrated. If the combined ratio for the misrated policy was 160 percent, the company would have paid $1,600 in costs. Correcting the error increases premium, and the policy doesn’t renew. The company now has 84 policies that renew for $84,000 in premium, expected costs of $83,400 ($85,000 minus $1,600), and a combined ratio of 99.3 percent ($83,400/$84,000).
The company’s retention decreased one percentage point, from 85 percent to 84 percent, but its profitability improved. The combined ratio decreased from 100 percent to 99.3 percent. If companies gear corporate goals toward retention without regard to profitability, management has less incentive to take action on underpriced policies, potentially resulting in lower retention.
The better strategy is to balance retention with profitability. Improving data accuracy leads to a virtuous circle of prices, whether raised or reduced, that more closely match actual risk. Such practices enhance the insurer’s competitive position compared with less sophisticated insurers.
Risk management strategies
To improve operating performance that results in long-term financial success, a company must adopt risk management strategies that address issues in each segment. One approach to implement such strategies is to segment the book by risk of flight and profitability.
Because the company has the policy on its books, it should have enough information to determine its profitability. It’s possible to develop scoring models that predict “risk of flight” (inverse of retention). Figure 1 below shows actual and predicted retention rates for a retention model based on data from a mix of diverse Verisk Insurance Solutions – Underwriting clients. The “directional fit” of the retention score is extremely good. Insurers can use the model as an excellent segmentation tool before renewal.
Operational processes can address the core issues in each segment of the business. Table 2 (below) shows the operational and strategic challenges by segment. That strategic differentiation will produce better long-term performance — and a core advantage for the business.
Table 2: Risk management strategies by segment
For superior financial performance, companies must adopt a risk management strategy that looks at the connections among all aspects of the business. Key requirements are recognition of all risk factors and accurate data collection through an ongoing rating integrity process. Accurate data results in better actuarial and risk analysis, better risk differentiation, better pricing, and better retention and risk management strategies. Those improvements enhance an insurer’s competitive position and allow it to grow profitably.
Short-term strategies that look only at the immediate problem, whether a focus on retention to the exclusion of other fundamentals or saving money by cutting costs on programs that manage data integrity, are just that — short-sighted. Especially with the escalating emphasis on enterprise risk management, insurers must consider a longer-term horizon. That’s easier said than done given the typical compensation structures that exist today. Many insurance companies provide their executives with incentives that emphasize certain ratios over others. For example, companies may narrow their focus to the underwriting expense ratio or retention ratio and lose sight of overall profitability. With the advanced analytic and underwriting tools available today, it’s possible for any insurer to manage appropriate retention and strong underwriting goals — and significantly improve financial performance.
This article originally appeared in Verisk Review and may have been modified for republication.
Raj Bhat, vice president, Verisk Insurance Solutions – Underwriting, has more than 30 years of experience developing and providing solutions, services, and high-level management consulting to the property/casualty industry. He has also held senior-level positions at Quality Planning (QPC), which is now part of Verisk Underwriting, and ADP Claims Solutions (Audatex). Dr. Bhat earned a Ph.D. in business from the University of California, Berkeley.