Every quarter when I look at the combined operating ratioresults for the P&C claims industry, I see a constant problem:increasing loss adjustment expenses (LAE). 

To combat this trend, many insurance carriers have begunautomating or outsourcing tasks like vehicle collision damage appraisals. As a  resultwe're seeing an increase in the volume of these appraisals"outsourced" to direct repair program (DRP) partners, likely in aneffort to avoid pricey  insurance company or independentstaff appraisals. The cost of a staff appraisal can be the mostexpensive collision repair appraisal channel, potentially moreexpensive than an independent appraisal — hence the shift.

Direct repair appraisals have two advantages: lower costs, and apotential to reduce claims cycle time if the vehicle is left forrepairs and repairs begin promptly.

Carriers can make this transition by automating portions of thedirect repair process, which can help make it the quickest andtherefore most cost effective appraisal channel  whilealso allowing it to be scalable to meet increasing volume.

It's also important to implement electronic auditingvia  a "pass and pay" feature, which lets DRP reviewersfocus on appraisals that really need attention rather thanindividually reviewing 100 percent of repair claimsvolume.  Simplifying tasks such as matching electronicphotos to repair orders can help with efficiency, as well.

Not everyone is a fan of using DRP-supplied appraisers though. Many feel that DRPseverity rates run higher than staff appraisal average severity,but this is not always the case because you are measuring twodifferent areas.

A DRP average severity is the average payment it took (withsupplements) to repair a car. Staff appraisals are frequently"cashed out" and owners choose not to repair their vehicles. If youcompare staff and DRP appraisal data sets by $100 increments, youwill likely see that DRPs don't have many repairs that total under$500, as in the case of typical staff appraisals, which is wherethe higher average DRP repair average is coming from.

When was the last time you looked at your data in this way?

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