Consistent claims management from the very beginning of
the claim lifecycle reduces the need for incremental
increases of loss reserve. Predictive analytics can be used
to more accurately calculate the case or loss reserve by
comparing a loss with similar claims. Also, whenever the
claims data is updated, predictive analytics can be used to
re-assess the loss reserve. Improved loss reserving
accuracy enables insurers to move funds from bulk
reserves into more flexible investments.
In addition, organizations have experienced
a 35% adjuster efficiency which can amount
to an annual saving of $600k for processing
2,500 claims.
Model 1 – Litigation Identification
The cost of defending disputed insurance claims represents a significant portion of a
company's loss expense ratio. Every claims manager can relate to the typical "horror story"
claim, where the employee has a minor "slip and fall" breaks a finger and walks away with a
$250,000 settlement.
In fact, research has found that litigated claims and settlements have
increased 10-fold in the last 5 years.
Litigation optimization enables insurers to use predictive analytics to calculate a litigation
propensity score. Claims that involve an attorney can be as much as four times higher
compared to non-litigated claims. Analytics can help insurers determine which claims are
likely to result in litigation and assign those claims to more senior adjusters who can
hopefully settle the claims sooner and for lower amounts.
Model 2 – Case Reserving
The first ongoing problem with managing claims leakage comes down to one simple thing:
claims managers often have no effective way of predicting the size and duration of a claim
when it is first filed. Accurate loss reserving and claims forecasting is essential for claims
departments, especially in "long-tail" claims like liability and workers compensation.
OPTIMIZING CLAIMS | 4
