Model 5 – Claims Severity Score
The risk management and insurance industries
are facing a crisis in the shortage of qualified
experts. In a recent survey, 25% of respondents
said that they have less than 5 years of workers'
compensation industry experience. With the
declining number of experienced claims
adjusters, the initial assignment of a claim to the
right resource is more important than ever.
Claims are usually assigned to adjuster at the
First Notification of Loss (FNOL) or First Report of
Injury (FROI) stage using business rules based on
limited data. This unscientific approach often
results in high reassignment rates that impacts
the loss adjustment expenses, the claim duration,
settlement amount and negatively affects the
claims experience. Using predictive analytics to
create a claims severity score ensures that
priority claims receive priority treatment. By
implanting machine learning techniques to
cluster and group loss characteristics (such as
loss type, location and time of loss, etc.), claims
can be scored, prioritized and assigned to the
most appropriate adjuster, based on experience
and loss type. High severity and more complex
cases are assigned to the most qualified adjusters
while low-exposure claims are channeled to less
experienced adjusters or in some cases
automatically adjudicated and settled.
Additionally, analytics is helping claims managers
to measure the effectiveness of the claims
handling process, in particular adjuster efficiency.
Traditionally adjuster productivity had been based
on an open/closed claims ratio. The objective was
to close more claims than are open each
reporting period. Analytics enables insurers to
produce Key Performance Indicator (KPI) reports
to measure adjuster performance based on
customer satisfaction, overridden claims
settlements and other related metrics.
OPTIMIZING CLAIMS | 6
