COUNTY REINSURANCE LIMITED 2015 ANNUAL TRAINING MEETING CATASTROPHIC DATA QUALITY NOVEMBER 18, 2015 Richmond W. Wall Senior Vice President Marsh Risk Consulting

Course Outline Modeling 101 Examination of the Effects of Uncertainty on Modeling Modeling’s Potential Impact on Placement Critical points in the process CatDQ Conclusion December 1, 2018

Modeling 101 – Be Certain to Avoid Uncertainty Modeling inputs are: Primary Characteristics – Allow the model to select the base vulnerability. These include: Construction Occupancy Year Built Number of Stories Secondary Modifiers – Modify the base vulnerability up or down. These can be considered the details of the construction of the structures or assets. NOVEMBER 18, 2015

Examination of the Effects of Uncertainty on Modeling 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. The poor modeling data will typically result in higher mean losses and standard deviations or coefficients of variation (CV’s) which can increase loss expectancies. NOVEMBER 18, 2015

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 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 reducing the AAL’s, the premium levels can also be reduced significantly. Underwriters may also increase premium to account for the additional uncertainty associated with a portfolio due to poor data quality. December 1, 2018

CatDQ MRC developed CatDQ in response to the concentration by the underwriters on data quality. Creates professionally validated modeling information. Site visits are conducted at locations that are: Loss drivers Critical to operations Otherwise suspect Upgraded data is modeled and provided to markets. NOVEMBER 18, 2015

CatDQ Deliverables Provided Individual Construction and Coding Reports are provided for each surveyed location. These reports include the coding for all primary and secondary characteristics for the locations. The enhanced input data for the CatDQ locations developed from the site visits are included in the modeling data provided by Marsh in Excel as well as the EDM. If this data is utilized in your modeling the results should match what Marsh produces. NOVEMBER 18, 2015

CRL CatDQ Locations Locations for CatDQ are chosen according to the location level average annual loss as well as a consideration of the coding and how the location is modeling. CRL’s first CatDQ in 2012 included 11 locations in the Glynn County, GA that drove 26% the hurricane risk expressed in terms of the AAL to the Georgia portfolio. The second project included 3 locations in Dare County, NC and 3 locations in Davis County, UT. NOVEMBER 18, 2015

History of Success 2013 Results Reduced the AAL for the 11 locations by 72% which resulted in an overall reduction in AAL of approximately 20%. Removed approximately $300,000 worth of AAL for this portfolio. 2015 Results For the CatDQ Locations EQ AAL’s decreased from 33% For the CatDQ Locations HU AAL’s decreased from 73% NOVEMBER 18, 2015

Examples of CatDQ Findings Dare County Admin Building Originally coded as FIRE 4 – Masonry Non-Combustible which maps to RMS 2 – Masonry. Building is a Steel Frame with a Steel Roof building that should be coded as RMS – 4C. NOVEMBER 18, 2015

Examples of CatDQ Findings Weber Center - Ogden, UT Originally coded as Fire 4 – Masonry Non-Combustible. This maps to Unreinforced Masonry Building is a reinforced Masonry Shear Wall building that should be coded as ATC – 7. NOVEMBER 18, 2015

Conclusion Premium for Catastrophic Natural Hazard Capacity is linked very closely to Average Annual Loss generated by the CAT Models. Generally a large portion of the AAL for a portfolio is driven by a small portion of the total portfolio. By improving the input data for the portfolio, the AAL can be significantly reduced, which should result in premium savings. Many times the savings will be more than the reduction in AAL due to the premium multiplier. By providing reports that document the process used to determine the coding the uncertainty the underwriter has around the portfolio will be reduced and should result in additional savings. Finally, always remember…”Be Certain to Avoid Uncertainty” NOVEMBER 18, 2015