* Allow the Chief Underwriting Officer (CUO) to make the
best informed decisions about catastrophe limit requirements and
budget for per-risk and per-event retentions.
* Increase competition by maximizing the number of
treaty markets willing to quote.
* Minimize treaty restrictions driven by data quality
(actual or perceived).
* Increase confidence of rating agencies in exposure data.
* Maximize the CUO’s leverage for negotiations with
treaty underwriters.
* Reduce operating revenue leakage from risk misclassification,
underinsurance, and failure to comply with existing underwriting
guidelines.
* Reduce risk of missed or inadequate facultative reinsurance.
* Improve profitability resulting from better decision-making.
Background
* Catastrophe models drive pricing for catastrophe exposed risks.
These models are sensitive to exposure data quality and
undervaluation.
The loss estimates can be improved with more complete data.
* If critical data is missing, the underwriting process will either
be suspended pending receipt of the missing data, or
the underwriter may fill-in the missing data based on assumptions
(which are likely to be conservative), or
the submission may simply be declined.
* Exposure values being presented to underwriters are commonly
30-40% under the actual replacement costs.
Many underwriters systematically compensate for underinsurance
by inflating values used in their internal analysis, or
by relying on the actuaries to build in rate adjustment factors.
If the carrier is doing a better than average good job of keeping
the values current, they need to communicate this so that
the treaty underwriters can use the values as presented with
greater confidence.
* If the treaty underwriters lack confidence in the quality of
the information being presented, they will compensate for the
increased uncertainty by declining to quote or by reducing
their willingness to give the best terms and conditions available.
* The willingness of the underwriters to accept the data as
submitted is a function of the current soft-market environment,
and the market conditions can change very rapidly following a
large event (Earthquake, Hurricane, or other catastrophe).
* Underwriters may already be compensating for poor quality
data and undervaluation in their current pricing. Improving the data quality and correcting the values will
not necessarily result in higher treaty costs!
* The same data that feeds the treaty underwriters’ analysis
also feeds the CUO’s decision-making process.
Undervaluation and incomplete/inaccurate data lead the CUO to
underestimate limit requirements, and also prevents the broker
from creating optimal program layering.
The Asperta Process
1. Review the existing portfolio data; check for completeness,
internal consistency, format, and perform high level analysis
for valuation.
2. Review the current treaty contract wording and structure
This can highlight restrictions being driven by perceived data
quality and be a factor in prioritization of work in later phases.
3. Interview underwriters (incumbent and prospective) to discuss
their specific data needs (these can change over time).
4. Prepare a proposed action plan for supplemental data collection
and reformatting.
5. Establish agreement on the action plan between CUO,
reinsurance broker, underwriters, and other involved parties.
Asperta will document scope of work in detailed work-order.
6. Gather supplemental data as needed.
7. Generate current estimates of building, contents, and
business interruption values.
8. Prepare updated submission spreadsheet(s) and related data,
including narrative description of the process.