When considering the risk of agent
antiselection, it is vital to compare
risk-scoring results of individual
agents to the risk-scoring results of
agents as a group. These results will
include the attribution of predictive
modeling scores and traditional medical underwriting scores associated
with final underwriting
decisions. By analyzing the distribution of
risk scores, insurers can
identify and flag agents
who are at the extremes.
Remediation steps to address anti-agent selection
can include, among others, changing underwriting rules and instituting
a comprehensive evaluation of the
agency force.
In some circumstances, reinsurers
may be unwilling to offer the same
competitive reinsurance rates for un-
derwriting that utilizes nontraditional
sources of data. If this happens, life
insurers may consider applying busi-
ness rules derived from predictive
modeling only to the underwriting re-
quirements for policies beneath their
retention limit. They also can try
to negotiate experience refunds for
business underwritten using predic-
tive models. The experience refund
will allow the insurance company to
secure reinsurance coverage through
its preferred reinsurance provider at a
higher premium than those using tra-
ditional underwriting methods. If ac-
tual experience develops as expected,
however, the life insurance company
will get a refund on the higher pre-
mium. Over time, the reinsurer will
see that underwriting is meeting com-
pany expectations, and the need for
the higher premiums and experience
refunds eventually should diminish.
In some circumstances, reinsurers
may be unwilling to offer the same
competitive reinsurance rates for
underwriting that utilizes
nontraditional sources of data.
GARY CIARDIELLO is a principal in the insurance and actuarial advisory services practice
at Ernst & Young LLP. DAVID McLEROY is
an executive in the insurance and actuarial advisory services practice at Ernst & Young LLP.
