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

SPEAKERS Brett Gillmon, ACII Mark Crites, ASA, MRICS Rich Wall, CBCP Managing Director Marsh Southeast Partnership Property Practice Brett.Gillmon@marsh.com 404-995-2484 Mark Crites, ASA, MRICS Senior Vice President Marsh Valuation Services Practice Mark.Crites@marsh.com 404-995-2594 Rich Wall, CBCP Marsh Risk Consulting Richmond.W.Wall@marsh.com 404-995-2976

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

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

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?

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

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)

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)

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”

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

Valuations

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

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

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

Data quality

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.

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).

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.

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.

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.

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.

Questions?

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