The current homeowners underwriting environment is exceedingly complex. To ensure dependable and consistent underwriting decisions, companies are incorporating more and more risk-specific information–such as fire-protection class, claims/loss histories and crime reports–into their underwriting decisions.
Straight-through processing–where the vast majority of underwriting decisions are made by programmed rules–is becoming more common. As a result, there is an increased demand to automatically incorporate risk-specific information into the underwriting environment.
For those insurers involved in catastrophe risk, the common risk metrics are hazard assessments such as distance to the coast or the nearest fault, county-level restrictions on total insured values and territorial ratings. These metrics, while useful, do not provide a complete view of the catastrophe loss potential.
A better view requires a detailed catastrophe risk analysis that accounts for the potential impact of a peril on the actual construction characteristics of a property based on its geo-coded location.
Fortunately, with minor modifications to current business practices, most insurers can easily incorporate detailed catastrophe analyses in their underwriting decisions. The first step is to enhance the data collected during the underwriting process.
AIR Worldwide recently conducted a survey of U.S. primary insurers to determine how many companies capture and maintain detailed data–such as total living area, exterior wall type, roof shape and foundation–on residential properties.
Of those queried:
o Only 25 percent of respondents said their companies collected such data.
o 53 percent said they did not.
o The remaining 22 percent were unsure.
These results are not surprising considering that many companies only focus on gathering data–such as address and year built–that is relevant to their rating algorithms. When developing a complete view of the catastrophe risk, however, this limited data set is often insufficient.
Although many companies do not maintain the property data needed for reliable estimation of catastrophe risk, most do handle it during the underwriting process to estimate the property’s replacement cost.
A typical workflow for homeowners insurers includes collection of property data by insurance agents, customer service representatives and property inspectors. These individuals estimate a replacement cost, and each one has a vested interest in capturing as much property-specific data as possible to provide clients with a reasonable replacement cost estimate.
Since the data needed for detailed catastrophe risk analyses is already used at most companies, the challenge is to capture the data in a useful form and seamlessly link this data to the catastrophe modeling system.
Most companies do not run detailed catastrophe risk analyses on residential properties because they require the policies under consideration to be taken out of the automated underwriting environment and placed in the hands of a catastrophe modeling specialist–an exchange that slows the underwriting process.
This is where technology is having a major impact. Today, systems are available that allow underwriters to conduct a detailed catastrophe analysis directly within the residential replacement cost estimator.
By eliminating the need to manually export and import data from one system to another, personal lines underwriters can easily analyze catastrophe risk at the point of underwriting.
In addition, property data, replacement cost estimates and catastrophe analysis results can all be maintained in the insurers’ database so that the risk can easily be reassessed at renewal.
A key result of detailed catastrophe analyses for homeowners insurers is the average annual loss (AAL). This value is the expected average loss per year over a period of many years.
Significant catastrophes will not happen every year. Therefore, it is important to emphasize that the AAL represents the expected loss over the long term.
Since underwriting rules vary by carrier, each insurer will likely use a different method to incorporate the AAL into its underwriting process. Approaches to incorporate AAL include:
o Simple issue/decline decisions:
By knowing the AAL for each risk as it is being underwritten, insurers are equipped to assign automated “go” or “no-go” decisions based on the AAL value itself.
o Automate underwriting rules using the expected catastrophe loss ratio:
The catastrophe loss ratio aids insurers in determining the break-even point for each risk, permitting companies to select only those risks for which a profit is expected. The expected catastrophe loss ratio for a risk is calculated by dividing the AAL by the premium.
o Feed AAL into rate-scoring models:
Many insurers underwrite residential risks using risk scores that incorporate several property-specific risk metrics. Adding the AAL to a scoring model enables insurers to account for one of the most infrequent but potentially devastating perils.
o Select rating plan:
For insurers using multiple rating tiers for an individual risk, the AAL can be a factor in determining the appropriate tier.
o Adjust price under consent-to-rate or excess and surplus plans:
For insurers with more flexibility in pricing risks, the AAL can become a significant driver in determining the policy premium. This provides an opportunity for insurers to reflect the true catastrophe risk of the property in the premium, potentially allowing an otherwise uninsurable property to find coverage.
In the wake of the 2004 and 2005 hurricane seasons, many insurers learned that poor catastrophe risk management can severely impact the well-being of their company.
Fortunately, catastrophe risk management can now be effectively integrated into an automated underwriting environment. This will enable insurers to improve underwriting decisions by quickly and easily assessing the catastrophe risk of individual residential properties before they are added to the portfolio.
“By eliminating the need to manually export and import data from one system to another, personal lines underwriters can easily analyze catastrophe risk at the point of underwriting.”