reserves initially set in claims in certain
categories are fairly accurate when the
claims are eventually settled, or if they
are wildly off-base. Inaccurate reserving
can be a serious problem for an insurer
or self-funding entity. If the adjusters are
simply slapping a standard reserve on
each new claim without ever updating it
to reflect what the reserve should be, all
sorts of mayhem can result.
When reserves are set too high, there
is a tendency to settle claims for more
than their value. It’s the old “get rid of it”
philosophy, but that can be expensive.
On the other hand, if reserves are set too
low, when the facts about the claim and
the demands for high amounts of settle-
ment come in, then the reserves have to
be jacked up, distorting the claim pat-
terns. Further, it tends to lead to unnec-
essary litigation. The adjuster may try to
low ball the settlement offers to save face
for having set too low of a reserve and
end up setting a more realistic claim re-
serve along with a legal expense reserve.
If claims are being handled in that man-
ner, then they might as well be handled
by a computer, as the adjuster becomes
superfluous, hence unemployed.
One advantage to using a RMIS is that data can be
mined for a variety of claims-related factors.
ning wheels. Management, either in an
insurance company or in a self-funding
commercial entity, needs to mine their
loss data for trends or details in the aggregate. The macro approach is how
the overall picture can be analyzed for
problems and possible solutions to those
problems. Most claims and risk managers
don’t have time to look at every individual loss, although they should be looking
carefully at any loss where the reserve
may trigger an excess exposure.
Likewise, no individual adjuster can
be aware of the aggregate loss exposure.
When an insured has a primary policy
excess layer insurers. Further, the adjuster
may not know the terms of the agree-
ments as to how allocated expenses (such
as defense costs) will impact the retained
risk or deductible amounts. Loss data re-
ports can keep track of such details.
Ken Brownlee, CPCU, is a former adjuster
and risk manager based in Atlanta, Ga.
He now authors and edits claim-adjusting