Weathering the Storm. Steve Drews, Impact Forecasting & Paul Eaton, Aon Benfield Analytics Analytics Insights Conference July 22-24

Transcription

1 Weathering the Storm Steve Drews, Impact Forecasting & Paul Eaton, Aon Benfield Analytics 2014 Analytics Insights Conference July 22-24

2 Agenda Section 1 Section 2 Section 3 IF s New Historical Severe Thunderstorm Model: STS RePlay Experience Analysis Enhanced Tornado Viewing Guide 2

3 Agenda Tracker Section 1 Section 2 Section 3 IF s New Historical Severe Thunderstorm Model: STS RePlay Experience Analysis Enhanced Tornado Viewing Guide 3

4 Why Not are enough the current emphasis severe thunderstorm stochastic on or a lack of recent catastrophe models so far off historical with their AAL hazard values???? data 4

5 Annual Severe Weather Frequency By Different Periods Average Annual SPC Reports: All Tornadoes Average Annual SPC Reports: All STS Perils 35,000 Reports / Year 28,642 29,638 30,000 26,931 25,000 20,000 15,000 11,271 10,000 5, Reports / Year 1,500 1,284 1,283 1, , Average Annual SPC Reports: All Hail Reports / Year 15,000 12,588 13,461 13,858 10,000 5,206 5,000 0 Average Annual SPC Reports: All Convective Wind Reports / Year 20,000 13,059 13,898 14,444 10,000 5,

6 Annual Severe Weather Frequency: Tornadoes The vast majority of tornado frequency increase stems from weaker (and not as easily detectable) tornadoes Note: 2013 lowest tornado total since

7 Annual Severe Weather Frequency: Hail The vast majority of hail frequency increase stems from smaller hail occurrences 7

8 Annual Severe Weather Frequency: Wind Both weaker AND stronger thunderstorm-produced winds continue to show frequency increases 8

9 What if there was a severe thunderstorm catastrophe model that would more accurately predict my AAL? Well, now there is 9

10 New IF ELEMENTS Severe Thunderstorm Tool: STS RePlay Based on recent historical data obtained from the Storm Prediction Center Contains last 10 years of severe thunderstorm activity Contains tornado, hail and convective wind occurrences Recent historic experience contains a richer catalog of activity based on better data capture using Doppler radar and other technological advancements ELEMENTS STS RePlay event set Can be used for a replay of a large catalog of historical daily outbreaks to better understand loss behavior A replay of historical event catalog against current exposures allows a recast to current loss expectations RePlay catalog can be used for obtaining a view of average yearly losses as an alternative to Average Annual Losses produced by stochastic models Note: the scenario set is not a replacement for stochastic models, but is an alternative recast view using actual historical experience 10

11 STS RePlay Source Data Source data: Storm Prediction Center Local Storm Reports (LSRs) Gathered from trained storm spotters, emergency managers, law enforcement, automated weather stations and Doppler Radar Three types of severe weather data gathered All tornadoes Hail 1 or greater Convectively-induced (thunderstorm-produced) winds of 58 mph or greater Reports characterize severe weather occurrences Date and time, location (lat/long), etc. Intensity, damage, etc. Data used Total of 260,610 LSRs analyzed and converted into ELEMENTS format after initial filtering (hail below 1, winds below 58 mph, reports with incomplete data) 11

12 STS RePlay Input Data Occurrence Counts Tornadoes: 13,764 (average 1,376/year) Hail: 125,924 (average 12,592/year) Wind: 120,922 (average 12,092/year) 12

13 STS RePlay Event Set Generation Hail and Wind Data usage within SPC LSR database files Start coordinates Hail Size Wind (knots) Directionality calculated as a random number due to the uncertainty of the direction (i.e. no end coordinates) Path Length and Path Width calculated as a random number based on average, lower bound, upper bound, and standard deviation values given within the ELEMENTS Severe Thunderstorm Model stochastic event set for each hail size bin (0-5) and wind intensity bin (0-3) Hail Size calculated using hail size provided by SPC and converted from inches to millimeters If hail size is less than 1 (25.4 mm), data is not used Wind intensity calculated using wind speed provided by SPC and assigned to Fujita Scale If wind intensity is less than 50 knots (58 mph) data is not used 13

14 Special Considerations For Wind and Hail SPC Reports Limited to point estimates No end coordinate reported in SPC data No data on swath shape (length, width) SOLUTION: Implement current inplace Impact Forecasting research on characterization of hail and wind swaths Multiple Hail Reports Multiple reports could represent portions of same event swath Overlapping swaths over-count losses SOLUTION: Bin events by report time (5 minute increment) and spatial proximity (0.6 mile increment) and treat similar reports as duplicates of the previous reports Single Hail Swath 14

15 STS RePlay Event Set Generation Tornadoes Data usage within SPC LSR database files Start and end coordinates Intensity (Fujita Scale/Enhanced Fujita Scale) Path Length (verification only) Path Width Directionality calculated by utilizing the start and end coordinates given by SPC data Path Length calculated by utilizing the start and end coordinates given by SPC data and converted into kilometers Output verified by SPC data values Path Width (maximum) taken directly from SPC data and converted into meters (given in yards) 15

16 Special Considerations For EF3+ Tornado SPC Reports Strong to violent tornadoes (EF3+) often long lived with complex track shape and varying intensity Example: Joplin, MO 5/22/2011 EF5 tornado SPC LSR data characterizes start and end coordinates and single intensity rating Example: Joplin EF5 as straight line between SPC start and end coordinates 490 EF3+ events in data set All 490 EF3+ events researched Majority didn t have detailed information 28 EF3+ tornado tracks modified Ad hoc research for event shape and intensity (NWS-sourced official track data) Re-parameterized events as necessary for proper representation within event set 16

17 Hazard Uncertainty Two mile tolerance for coordinate accuracy creates uncertainty in LSR event location Street-level addressing not available cross-street coordinate assignments most likely SOLUTION: Sample every LSR 25 times, adding position uncertainty 5% position uncertainty for tornadoes (more accurate reporting) 20% position uncertainty for hail and wind 6.5 million event set generated in ELEMENTS from 260,610 individual LSRs 17

18 STS RePlay Results Verification Comparison of Severe Convective Storm Models To Actual Client Historical AALs Client Client Size* IF STS RePlay to Actual Stochastic Model A to Actual Stochastic Model B to Actual Stochastic Model C to Actual A Large B Large C Small D Small E Large n/a F Large n/a Weighted Average * Large client actual loss approx. 10x small client actual losses 18

19 IF ELEMENTS STS RePlay Summary Designed only as an alternative AAL view using rich data due to technological improvements in data capture and an overall active severe convective weather period Contains historical SPC occurrences of tornadoes, hail and convective winds from active 10-year period Each severe weather occurrence is sampled 25 times to account for hazard position uncertainty Reports were analyzed spatially and temporally to eliminate duplicate reports Significant tornadoes were analyzed and modified (where information existed) to better represent the overall track and intensity fluctuations that EF3+ tornadoes often exhibit Over 6.5 million severe weather occurrences are contained within the STS RePlay event set Official Release: September 2014 (in coordination with new IF STS Stochastic Model) 19

20 Agenda Tracker Section 1 Section 2 Section 3 IF s New Historical Severe Thunderstorm Model: STS RePlay Experience Analysis Enhanced Tornado Viewing Guide 20

21 What if I Instant Have Need Want Want Don t noisy to a have realistic see quantifiable data Derrick experience credible that assessment makes Rose basis loss that fast for trend of experience selections my comparing break represents average through and because stochastic annual on-leveling my the current I ve loss entire model grown from very Mavericks underwriting severe output into new thunderstorms? difficult? to defense experience? regions? paradigm? again? 21

22 Severe Weather Experience Templates Superior Trend Diagnostics Daily Incurred Losses Log Scale 100,000 10,000 Loss Thousands 1, Trend: 8.2% 1 Jan-98 Jan-00 Jan-02 Jan-04 Jan-06 Jan-08 Jan-10 Jan-12 Date Daily Average Severity Log Scale Trend: 9.1% Trend: 0.0% 100,000 Daily Claim Counts Log Scale 10,000 10,000 Severity 1,000 Claims 1, Jan-98 Jan-00 Jan-02 Jan-04 Jan-06 Jan-08 Jan-10 Jan-12 Date Trend: 8.1% 10 1 Jan-98 Jan-00 Jan-02 Jan-04 Jan-06 Jan-08 Jan-10 Jan-12 Date Trend: 0.2% 22

23 Severe Weather Experience Templates Analysis Of Daily Loss Data Automated Aggregator of Events Per Occurrence XOL & Aggregate Structure Analysis Cat XOL Aggregate Optimization of Program Co-Part 5% Per Event Deductible 2,000,000 Limit 210,000,000 Deductible Type Standard Trended Retention 20,000,000 Per Event Limit 18,000,000 Selected Occ Ceded Max ceded through day n assuming an event ends on Max ceded Events, Gross Event Loss to Event Loss to Incurred AccidentDate DOY 96 Hour Total Regular XOL Loss Regular XOL Franchise XOL Ceded Loss Total Ceded day n through day n of Reinsurance Per Occ Layer Agg Layer 31, Jun , ,128,895 3,128, , Jun , ,128,895 3,128, , Jun , ,128,895 3,128, , Jun , ,128,895 3,128, , Jun , ,128,895 3,128, , Jun , ,128,895 3,128, , Jun , ,128,895 3,128, ,286, Jun ,522, ,522,215 7,522,215 5,522,215 5,522,215 8,651,110 8,651,110 7,522, ,522, , Jun ,457, ,457,260 7,457,260 5,457,260 5,457,260 8,586,156 8,651, , Jun ,491, ,491,894 7,491,894 5,491,894 5,491,894 8,620,789 8,651, , Jun ,521, ,521,013 7,521,013 5,521,013 5,521,013 8,649,908 8,651, , Jun , ,651,110 8,651, ,402, Jun ,593, ,758 2,593, , ,758 9,244,868 9,244,868 2,593, ,758 23, Jun ,549, ,545 2,549, , ,545 9,200,655 9,244, , Jun ,510, ,226 2,510, , ,226 9,161,336 9,244, , Jun ,503, ,044 2,503, , ,044 9,154,154 9,244, , Jun , ,244,868 9,244, , Jun , ,244,868 9,244, , Jun , ,244,868 9,244, , Jun , ,244,868 9,244, , Jun , ,244,868 9,244, , Jun , ,244,868 9,244, , Jun , ,244,868 9,244, IF RePlay Losses by day tracking Initial step to aggregate into 3 day step cumulative totals before and after an aggregate deductible Aggregation of daily losses via local optimization rule 23

24 Agenda Tracker Section 1 Section 2 Section 3 IF s New Historical Severe Thunderstorm Model: STS RePlay Experience Analysis Enhanced Tornado Viewing Guide 24

25 TVG Concentrations Are Tornado-Track Based TVG uses scenario or What if analysis Example: what if the Moore, OK tornado from 2013 hit other exposure concentrations St. Joseph Columbia Kansas City Sedalia St. Louis 2013 Moore, OK EF-5 Track Cape Girardeau Springfield Poplar Bluff 25

26 Accumulations By Census Statistical Area Metropolitan Micropolitan Rural Joplin, MO 2011 Historical Tornado Track Fayetteville Springdale Rogers, Conditional Kansas City, MO KS St. Joseph, MO KS Cape Girardeau Jackson, MO IL Springfield, MO AR MO Percentile TIV No. Events TIV No. Events TIV No. Events TIV No. Events TIV No. Events Max 39,466, ,366, ,297, ,878, ,048, % 17,585, ,453, ,470, ,553, ,348, % 5,412,883 1,191 7,266, ,639, ,988, ,483, % 3,771,730 2,381 3,195, ,609, ,723,276 1,617 2,267,785 1, % 1,475,490 Conditional 5, ,917 1,584 Kansas 512,595 1,237 City, MO KS 1,415,083 4, ,076 2,886 MEAN 2,466,163 2,836,071 2,137,388 2,872,752 1,582,815 Total Events 11,908 3,168 2,474 8,086 5,772 Metropolitan Percentile TIV No. Events Joplin, MO 2011 Historical Tornado Track Max 39,466, % 17,585, Conditional Poplar Bluff, MO Sedalia, MO Sikeston, MO Paragould, AR Hannibal, MO Percentile TIV No. Events TIV No. Events TIV No. Events TIV No. Events TIV No. Events Max 63,045, ,566, ,808, ,479, ,629, % 50,838, ,498, ,377,070 27,025, ,423, % 12,989, ,920, ,320, ,414, ,066, % 5,412,883 1, % 5,914, ,409, ,991, ,442, ,360, % 1,541, ,268, ,499, , , MEAN 4,923,481 3,920,615 3,515,049 2,743,152 1,351, % 3,771,730 2,381 Total Events 1,988 1,804 1,192 1,226 1, % Joplin, MO 2011 Historical Tornado 1,475,490 Track 5,954 MEAN 2,466,163 Conditional Macon, MO Anabel, MO Bertrand, MO Matthews, MO Bevier, MO Percentile TIV No. Events TIV No. Events TIV No. Events TIV No. Events TIV No. Events Max 42,836, ,361, ,734, ,353, ,226, % 42,046, ,361, ,734, ,614, ,226,224 1 Total Events 11, % 23,580, ,649, ,275, ,482, ,643, % 9,327, ,150, ,446, ,986, ,326, % 2,887, ,319, ,635, , ,186, MEAN 6,871,468 4,355,448 7,894,294 2,514,532 5,466,836 Total Events

27 Tornado Viewing Guide Top Accumulations Loss Scenarios Conditional Sikeston, MO Poplar Bluff, MO Sedalia, MO Lebanon, MO Kennett, MO Percentile TIV No. Events TIV No. Events TIV No. Events TIV No. Events TIV No. Events Max 70,566, ,802, ,827, ,964, ,750, % 59,718, ,327, ,565, ,725, ,633, % 28,595, ,891, ,989, ,740, ,024, % 19,031, ,505, ,558, ,858, ,517, % 8,337, ,080,895 1,082 3,506, ,674,278 1,015 3,058, MEAN 12,255,941 10,235,657 8,144,893 15,490,019 6,411,309 Total Events 1,196 2,164 1,810 2,030 1,422 US $ in Ones Mayflower, AR 2014 Historical Tornado Track Sikeston, MO Conditional Loss Potential Inside Track Percentile TIV Low Intensity (20%) High Intensity (50%) Max 70,566,784 14,113,357 35,283, % 59,718,096 11,943,619 29,859, % 28,595,964 5,719,193 14,297, % 19,031,244 3,806,249 9,515, % 8,337,714 1,667,543 4,168,857 MEAN 12,255,941 2,451,188 6,127,971 Total Events 1,196 US $ in Ones Mayflower, AR 2014 Historical Tornado Track 27

28 Major (EF4 to EF5) Tornado Days Per Century A Tornado Day is any day in which you see at least one EF4+ tornado within 80km 28

29 TVG Loss Ranges Using Historical EF4 & EF5 Event Frequency Exceedance Probabilities Modeled Other Wind Loss Potential Inside Track RMS AIR TIV Low Intensity High Intensity RiskLink Touchstone Return Period 100% 20% 50% Return Period v13.1 v1.5 1,000 58,500 11,700 29,250 1,000 46,842 45, ,008 9,402 23, ,035 39, ,813 7,363 18, ,864 30, ,456 5,091 12, ,335 20, ,656 3,731 9, ,786 16, ,830 2,566 6, ,237 12,338 Annualized Loss from EF4s and EF5s in TVG 675 1,687 Annual avg 21,658 22,996 Std dev 5,316 7,543 US $ in Thousands Assumptions: Tornado frequency uses NOAA Storm Prediction Center data TVG event frequency by smoothed historical average of EF4+ tornadoes is SPC data using 80km Gaussian filter US $ in Thousands Excess of 1,000 Year Return Period Excess of 100 Year Return Period Memphis Memphis Other Denver Denver Monroe Clovis Tulsa Monroe Other Tulsa Clovis 29

30 TVG Loss Ranges Using Historical EF4 & EF5 Event Frequency Exceedance Probabilities 2013 Other Wind Change RMS AIR TIV Loss TIV Loss 2011 RiskLink Touchstone Return Period 100% 50% 100% 50% to 2013 Return Period v13.1 v1.5 1,000 17,191 8,596 15,423 7,712-10% 1,000 12,575 13, ,590 7,295 13,045 6,523-11% ,318 10, ,360 5,680 10,042 5,021-12% 250 8,379 8, ,825 3,912 6,792 3,396-13% 100 6,274 6, ,888 2,944 4,996 2,498-15% 50 4,941 4, ,306 2,153 3,664 1,832-15% 25 3,821 3,417 AAL from EF4s and EF5s in TVG % Annual avg 6,585 3,351 US $ in Thousands Std dev 1,435 1,667 Assumptions: Tornado frequency uses NOAA Storm Prediction Center data US $ in Thousands TVG event frequency by smoothed historical average of EF4+ tornadoes is SPC data using 80km Gaussian filter Key Metropolitan Risks 2011 Key Metropolitan Risks 2013 Excess of 100 Year RP Excess of 1,000 Year RP Excess of 100 Year RP Excess of 1,000 Year RP Other Louisville Cincinnati Other Louisville Cincinnati Cincinnati Cleveland Cleveland Louisville Cincinnati Cleveland Cleveland Louisville 30

31 TVG Historical Tornado Shapes Moore, OK Tornado Joplin, MO Tornado Date: May 22, 2011 Rating: EF 5 Date: Rating: Mayflower, AR Tornado May 20, 2013 Date: EF 5 Rating: April 27, 2014 EF 4 Est. Max. Wind: 200+ mph Est. Max. Wind: 210 mph Est. Max. Wind: Path Length: 22.1 Miles Path Length: 17 Miles Path Length: 41.3 Miles Path Width: 0.75 Mile Path Width: 1.3 Miles Path Width: 0.75 Miles Fatalities 162 Fatalities 24 Fatalities mph 16

32 What if you could run every EF4 and EF5 tornado? 32

33 Stop Light Exposure Management 33

34 Paul s Everyone s Favorite Quotable Statistician Essentially, all models are wrong, but some are useful George E. P. Box 34

35 Biographies Steve Drews, Associate Director & Lead Meteorologist Steve Drews is Associate Director and Lead Meteorologist for Impact Forecasting. He currently heads the development of model customization for Impact Forecasting s catastrophe modeling suite, ELEMENTS. Over his 12 years with Aon, Steve has provided expert analysis on hurricane and tornado damage and has been a featured speaker at client, meteorological and government presentations about coastal urbanization, global climate change, tropical cyclone frequency, and severe convective weather frequency as well as provided scientific testimony on behalf of insurance and reinsurance companies in legal matters. Paul Eaton, FCAS, Director Paul Eaton has worked in the Aon Benfield Analytics Chicago office for seven years. Paul s current role focuses on catastrophe management including risk adjusted pricing and capacity allocation. Paul dabbled in telephony consulting, home improvement sales, and even driving an ambulance prior to joining Aon Benfield. 35

36 Contacts Steve Drews Associate Director & Lead Meteorologist Impact Forecasting Paul Eaton, FCAS Director Aon Benfield Analytics

37 Legal disclaimer: Impact Forecasting LLC The results listed in this report are based on engineering / scientific analysis and data, information provided by the client, and mathematical and empirical models. The accuracy of the results depends on the uncertainty associated with each of these areas. In particular, as with any model, actual losses may differ from the results of simulations It is only possible to provide plausible results based on complete and accurate information provided by the client and other reputable data sources. Furthermore, this information may only be used for the business application specified by Impact Forecasting, LLC and for no other purpose. It may not be used to support development of or calibration of a product or service offering that competes with Impact Forecasting, LLC. The information in this report may not be used as a part of or as a source for any insurance rate filing documentation. THIS INFORMATION IS PROVIDED "AS IS" AND IMPACT FORECASTING, LLC HAS NOT MADE AND DOES NOT MAKE ANY WARRANTY OF ANY KIND WHATSOEVER, EXPRESSED OR IMPLIED, WITH RESPECT TO THIS REPORT; AND ALL WARRANTIES INCLUDING WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE HEREBY DISCLAIMED BY IMPACT FORECASTING, LLC. IMPACT FORECASTING, LLC WILL NOT BE LIABLE TO ANYONE WITH RESPECT TO ANY DAMAGES, LOSS OR CLAIM WHATSOEVER, NO MATTER HOW OCCASIONED, IN CONNECTION WITH THE PREPARATION OR USE OF THIS REPORT. 37