The Insurance Elephant has a day job- as building consultant working with insurance carriers, sorting out vendor estimates, appraisals, unique damage cases, consulting on damage repairs, in the end addressing or confirming estimates of claim damages. It’s great work, and the Beast figures he is contributing to customer and carrier resolutions.
The realization is that property insurance (at least within the U.S.) has over the past several decades not only has evolved from a named peril, fire policy business, but to an all risk, ‘make the insured whole’, customer expectant of hand-holding, administration and audit heavy, huge cadre of adjusters, Gordian Knot of indemnification support.
Huge effort is expended by the industry to identify damage, estimate it to the nearest inch, arrange vendor assistance, babysit the outcome, audit every step, engage third party administrators, build nationwide networks of vendors, and massage every penny that is estimated for a claimed loss.
The Elephant lived within that jungle for a few decades and continues to make forays into the dense admin foliage, and finally realizes- changing the indemnity insurance model will not come from innovative inspection methods or virtual estimating- those changes simply have changed how the oversight burden is carried out.
The data are available to establish average payment amounts for high frequency, low scope losses- the attended (homeowner is present)- water backups, dishwasher leaks, toilet supply failures, toilet and tub overflows. These claims have common characteristics- limited damage to finishes (floors, walls, ceilings), some need for damage mitigation, and onsite inspection or virtual estimate based on vendor observations. These claims also are a frequent source of oversight ‘ping pong’- scope/valuation audits that result in settlement delays, audits that often focus on estimating practices that as audited result in minimal estimate effect but significant relative settlement delays.
A different approach to settlement for these high frequency, lower severity claims- establish a parametric limit that will be issued when a water loss as noted above occurs. There will be a need for automated verification of the claim (IoT devices come to mind), but not having a burden for all parties to prove damage and estimates for indemnification would have multiple benefits. Indemnity would not be an option, and the policy would be priced as such. Actuaries will cringe but the pricing of the policies might be better handled by data scientists who consult with actuaries.
Taking the thought process further, the new policy form could integrate parametric flood cover. For any property. The data and trigger methods are available now.
There are many perils that would fit this model- lightning, wind (minor damage), smoke, loss of power (or surges). Freeze losses where water release is limited. Mold claims.
Water releases from a failed toilet supply valve, the main valve monitor recognizes the toilet supply feed valve supplies more than 1.6 gallons of water and the bathroom occupancy sensor reflects a vacant room, causing the manifold valve to close, limiting the unexpected water release to 2.7 gallons. A simultaneous alert is sent to the person occupying the dwelling’s family room advising of the water on the floor in the bathroom. After the home’s monitoring system confirms the water has been cleaned up a survey is taken of the room’s floor, baseboard and relative humidity sensors to confirm readings that would support damage. Finding none, the system resets to normal and the dwelling’s owner sets the wet towels used to clean up the water into the laundry.
The policy form would need to change in how the insuring agreement is written (would need requirements for IoT or other monitoring devices), how coverage is afforded (would be a two stage cover- parametric and indemnity), parametric limits by peril, typical limits of liability for indemnity by cover- dwelling, other structure, UPP, ALE.
The condition for what an insured must do after a loss would change.
AI for anti-fraud would be employed.
Back-end audits (automated) to confirm the efficacy of the parametric decision, and algo revisions based on audited performance.
Trying to be more than experts in pricing shared risk has evolved into a post-claim over analysis, file review, third party administration audit shuffle, with clearly diminishing marginal returns for the extent of estimating anal retentiveness. Want to pare LAE and increase retention? Put the claim control back into the hands of the customer. Reduce the effect of the inconsistency of vendor estimates- parametric cover speaks of finite claim payments; vendors will need to adapt. The insurance industry needs to do its best work in developing what perils get what payments and stick with it. Let the repair industry price to meet the available claim payment through policies that leverage parametric models by peril.