0. PROPERTY SUBMISSION: DIFFERENTIATING YOUR PROGRAM AND SETTING THE TABLE FOR A STRONG OUTCOME
ATLANTA RIMS EDUCATIONAL CONFERENCE
FEBRUARY 9, 2017

1. SPEAKERS Brett Gillmon, ACII Mark Crites, ASA, MRICS Rich Wall, CBCP
Managing Director
Marsh Southeast Partnership Property Practice
Mark Crites, ASA, MRICS
Senior Vice President
Marsh Valuation Services Practice
Rich Wall, CBCP
Marsh Risk Consulting

2. Panel Discussion Outline
Outline purpose and key “submission” elements
The “forgotten” primary characteristic
“Be certain to Avoid Uncertainty”
Questions

3. The obvious Goal….. Competitive cost of risk transfer (Premium)
Optimal coverage terms and conditions (Balance sheet protection)
Partnership with aligned market (Stability and Performance)
Contract Certainty

4. How do you stand today?
On a scale of 1 to 10 how good do you think your submission is?
On a scale of 1 to 10, how much effort does your team spend in putting submission together?
Where do you focus most of your efforts?
Who drives your submission content and quality – Insured or broker?

5. It is about more than just the submission….
Define your exposure
Build connectivity to your brand
Develop and maintain key relationships
Continuous and focused improvement
Quality of the data
Evolving risk issues
Addressing market concern / interest

6. What do we mean by differentiation in the context of property program
“The submission is the Insured’s opportunity to make a first impression on their markets. A detailed, thorough submission is often a good indicator of the Insured’s general approach to risk management.” (Dom Aurino, Zurich Property Manager, South Region) “Complete and accurate data as part of the submission that is delivered in a timely fashion reduces uncertainty in the underwriting process; it is uncertainty with respect to the Insured’s data – particularly relative to catastrophe modeling - which is a major impediment to our ability to deliver even better results for our clients than we already do today” (Tim Meyer, Chubb Property Major Accounts, Southeast Region)

7. The Classic Submission
Statement of Values
Proposed Policy Wording &/or Coverage Specification
Detailed Loss Listing (minimum 5 yrs)
Risk Engineering Reports
Risk Mitigation Support:
BCP / DR plans;
Risk Improvement Plans;
Key Customer / Supplier information;
Background Information
Summary of business operations
Risk Management Philosophy
Exposure Methodology(ies)

8. What should be the priority?
Statement of Values (SOV):
Accurate Address information
Accurate replacement cost valuation for PD values
BI values consistently calculated year over year
Accurate and complete primary characteristics
Accurate and validated secondary characteristics
“The loftier the building, the deeper the ………. must be”

9. Broaden your view of “submission”
Meet annually with your risk transfer partners – about 2 months prior to renewal date
Identify potential site visits for both underwriters and their engineers
Include your subject matter experts in meetings or at the visits e.g. BCP or DR owner(s)
Articulate what you are seeking to achieve and why
Plan, Plan, Plan and allow sufficient time for the renewal process

10. Valuations

11. Valuations Common Mistakes Hot Buttons
Values are based in past mergers and acquisitions values
Too much reliance on financial records
Reporting of values for assets which are not insurable
Property policy wording (inclusions / exclusions) to not match real world asset situations
Macro and or inappropriate trends are utilized to update values
No Standard Operation Procedures in place to determine or update values
Collecting values from worldwide business units creates vernacular issues
Do not benchmark on a yearly basis or have an “appraisals” done with regularity
Do not provide enough detail in SOV

12. How Do Valuations Help Clients?
Before
Allow correct assessment of TCOR to aid in insurance buying decisions.
Address the adequacy of program and key sublimits, risk retention, and deductibles
By providing clarity, consistency over time, and transparency…
The insurer gains confidence in the underwritten assets, which helps in rate setting and negotiating.
Robust valuation methodology is recognized by underwriters as a positive risk factor.
Accurate property values allow insurers to establish credible loss estimates, determine how much insurance capacity they can provide, and develop pricing.
The quality of the modeling output is dependent on a number of factors, one being the values reported.
Values are a key characteristic in RSM modeling, PML, MFL, and other underwriting analysis.
Reduce the likelihood of over-inflated values, which can drive premiums up
Address loan covenant requirement.
Addresses a Person’s, Business’ or Board’s fiduciary duty to protect the underlying assets and value of an interest
Reduces the year-over-year effort of in-house staff to update OR distort values, whereas valuation (models once built) are easy to accurately update.
After
Mitigate application of co-insurance – values were correct
Mitigate a loss in confidence by the underwriting community in the validity of the overall values reported
Have a record of what is insured smoothing initial claims process

13. Accuracy of reported building and contents values
85% Misvalued
% Examples (n=186)
36% insured for less than 95% of the value
49% insured for more than 105% of the value
Comparison of Reported vs. Actual Value
Source: MRC VSP Analysis
FOR DISCUSSION ONLY

14. Data quality

15. Data Quality in Placement Process
CAT exposed placements are heavily dependant on modeling.
Modeling is heavily dependent on input data.
By extrapolation successful CAT placements are highly dependant on data quality.
Poor data quality can increase both the base losses as well as increase the uncertainty associated with the losses.
Since the modeled losses are produced at high confidence levels, the uncertainty in the modeling calculations can have a significant impact on the loss expectancies.
This characteristic of the models make it easier to lower loss projections than to increase them by providing additional input data.

16. Examination of the Effects of Uncertainty on Modeling
Modeling, like underwriting, is defined by uncertainty.
The lack of modeling data will typically result in higher mean losses and standard deviations or coefficients of variation (CV’s).
This increase in standard deviation normally results in increased loss expectancies and specifically average annual losses (AAL’s).

17. Uncertainty and Loss Expectancies
The greater the uncertainty the wider the bell curve around the mean loss expectancy.
When losses are presented at high levels of confidence the specified loss will increase even though the mean loss remains the same.
This characteristic allows us to utilize additional data in modeling and lower the specified loss even though the main loss may not change or may actually increase.

18. Modeling’s Potential Impact on Placement
Most insurance carriers utilize the average annual losses (AAL’s) to set or at least assist in setting premium levels.
AAL’s are driven by short return period events and as such are heavily impacted by input data quality.
For surge exposed properties, the AAL can normally be significantly reduced by providing a ground floor elevation for the facilities.
By working to reduce the AAL’s the premium levels can be reduced significantly.
Underwriters may also increase premium level over what the model drives to account for the uncertainty in what they are underwriting.

19. Examples of CatDQ Findings
Hotel in Florida
Originally coded as FIRE 3 – Non-Combustible which maps to RMS 4B – Light Metal.
Building is a Concrete Frame with a Concrete Roof building that should be coded as RMS – 3A.

20. Examples of CatDQ Findings
Caribbean Hotel
Originally coded as RMS 2 – Masonry for HU and EQ.
Building is a Concrete Frame with a Steel Roof building that should be coded as RMS – 3C for HU and ATC 6 – RC Shear Wall w/o Frame for EQ.

21. Questions?

22. This document and any recommendations, analysis, or advice provided by Marsh (collectively, the “Marsh Analysis”) are not intended to be taken as advice regarding any individual situation and should not be relied upon as such. This document contains proprietary, confidential information of Marsh and may not be shared with any third party, including other insurance producers, without Marsh’s prior written consent. Any statements concerning actuarial, tax, accounting, or legal matters are based solely on our experience as insurance brokers and risk consultants and are not to be relied upon as actuarial, accounting, tax, or legal advice, for which you should consult your own professional advisors. Any modeling, analytics, or projections are subject to inherent uncertainty, and the Marsh Analysis could be materially affected if any underlying assumptions, conditions, information, or factors are inaccurate or incomplete or should change. The information contained herein is based on sources we believe reliable, but we make no representation or warranty as to its accuracy. Except as may be set forth in an agreement between you and Marsh, Marsh shall have no obligation to update the Marsh Analysis and shall have no liability to you or any other party with regard to the Marsh Analysis or to any services provided by a third party to you or Marsh. Marsh makes no representation or warranty concerning the application of policy wordings or the financial condition or solvency of insurers or reinsurers. Marsh makes no assurances regarding the availability, cost, or terms of insurance coverage.
Marsh is one of the Marsh & McLennan Companies, together with Guy Carpenter, Mercer, and Oliver Wyman.
Copyright 2017 Marsh Inc.