Nat Cat-Risikomanagement in Echtzeit 1.087.024 Naturkatastrophen, 21.914 Verträge und eine Datenbank Dr. Kai Haseloh, Group Risk Management, Hannover Re Dritter Weiterbildungstag der DGVFM Hannover, June 16 2016
Nat Cat-Risikomanagement in Echtzeit 1.087.024 Naturkatastrophen, 21.914 Verträge und eine Datenbank
Dr. Kai Haseloh, Group Risk Management, Hannover Re
Dritter Weiterbildungstag der DGVFM
Hannover, June 16 2016
Real-time Exposure Management for a Reinsurer
Reinsurers (and large insurers as well) face the following task
They manage a book of hundreds, even thousands of reinsurance treaties
exposed to catastrophe risk
The portfolio shall make optimal use of available capital. This means:
• profitability and diversification are maximized
• within system of limits and thresholds which curbs the risk of overexposure
Standard since the early 1990s for measuring nat-cat risk: Nat-Cat Models
This talk is about the challenges of implementing a real-time exposure/risk
reporting IT system for a reinsurance portfolio exposed to cat risk
What this talk is about
Agenda
Cat-Modelling
• Exposure data available
• Building blocks of a Cat-Model
• Cat-Model Output and Risk Metrics
Cat Portfolio Management
• Portfolio Aggregation
• Challenges
Cat Modelling
Exposure Database for a Single Reinsurance Contract A large insurer may have 100,000s of locations re-insured against catastrophes
100,203
20,576
50,734
734
59,212
9,496 13,275
33,657
Primary Insurance Portfolios / Exposure Databases Can be as detailed as this...
Single-familiy residential
Replacement Value $500k
2 stories, built in 1979
Policy 126239R-1A
Earthquake insurance: $500k with 5% deductible
Filling station
Replacement Value $250,000k
Policy 6239C-1
Earthquake insurance: $200k with $10k deductible
Office complex
Replacement Value $100m
25 stories
Earthquake retrofits
Policy 2899A-1 Earthquake insurance: 10% of $50m with 5% deductible
Primary Insurance Portfolios / Exposure Databases But sometimes are as sparse as this...
State Sums Insured
Alaska 20,000
Alabama 162,850,000
Arkansas 12,970,000
Arizona 62,540,000
California 14,090,000
…
Washington 19,200,000
Wisconsin 125,000,000
West Virginia 51,140,000
Limit Range Number of Locations
Premium
Unknown 6,106 3,509,433
Under 500,000 77,851 64,995,721
500,001 - 1,000,000 20,556 39,775,647
1,000,001 - 1,500,000 7,456 20,965,224
1,500,001 - 2,000,000 4,255 14,309,799
2,000,001 - 2,500,000 2,622 10,331,667
2,500,001 - 5,000,000 5,454 28,681,663
5,000,001 - 10,000,000 2,807 20,091,749
10,000,001 - 20,000,000 881 9,249,410
20,000,001 - 30,000,000 289 3,735,154
30,000,001 - 40,000,000 88 3,407,222
Example 1: Surplus treaty Example 2: Per Risk treaty
Typical Reinsurance Coverages
Perils Covered
Earthquake
Flood
Wildfire
Storms
Lines of Business
Worker‘s Compensation
Marine
Business Interruption
Terrorism, Life, Personal Accident
Obligatory Reinsurance Types
Proportional
• Quota Share
• Surplus
Non-Proportional
• Catastrophe and Per Risk XL
• Stop Loss
Facultative Reinsurance
Coverage for very large individual
risks
Perils, LOBs, Obligatory and Facultative Reinsurance
Natural Catastrophe Modelling and Data Capture
Hazard
Event-
set generation
Local
intensities
Damage-
Estimation
Calculation of
insured Losses
Loss evaluation
Policy and
treaty conditions
Exposure Database
of
Insured Locations
Intensity Frequency
Vulnerability Financial module
Components of Natural Catastrophe Models Typical Design of a Cat Model
Nat Cat Know ledge for UWs
Components of a catastrophe model - Event Set The hazard is described by a set of discrete events
Events are a stochastic representation of
the catastrophe hazard
Generated by extrapolating historical record
• 20-100years ➠ 10000 years, or more
• Historical databases: HURDAT (U.S. HU),
JTWC (JP TY), USGS (North America EQ)
Events are described by their characteristics
• Storm: Track, Intensity, Windfield, Duration
• Quake: Epicenter, Magnitude, Direction, etc.
Where, how big, what type, and how likely?
Hazard
Event-
set generation
Local
intensities
Intensity Frequency
Historic Storm Tracks in the Atlantic Basin 1980-2005, Source: NOAA Best Track Archive
Components of a catastrophe model Event set - entire catalog
Event Year Day Event Info Cen Pres
Max Wind RMax Speed Angle Long Lat
2 1 214 Class 1 Hurr NC 987.1 73.4 36.4 11.2 18.1 -75.6 35.2
13 1 240 Class 1 Hurr GOM MX TX 981.4 78.8 11.9 13.2 -15.0 -97.5 24.8
32 1 270 Class 1 Hurr TX 982.7 70.1 22.4 6.3 -70.7 -96.6 28.2
41 2 276 Class 5 Hurr CU GOM BB JM MQ 905.8 164.6 15.8 14.3 -54.0 -82.9 17.0
70 3 291 Class 1 Hurr ME BD 987.8 76.1 29.5 51.8 -20.7 -69.0 44.1
83 3 290 Class 1 Hurr GOM TX 988.5 79.7 13.9 18.0 -61.0 -96.7 28.1
115 4 282 Class 1 Hurr LA MS 985.4 72.2 17.1 5.6 -42.8 -89.4 29.7
141 5 233 Class 1 Hurr LA 988.5 70.4 16.8 7.5 4.3 -90.3 29.1
146 5 257 Class 1 Hurr DR HT BF MQ CU 962.5 92.3 23.0 8.2 -23.2 -79.5 27.9
152 6 207 Class 2 Hurr GOM CU JM CJ AN 951.5 102.3 22.0 7.7 106.4 -84.7 28.8
156 6 223 Class 1 Hurr NC 977.7 81.7 42.3 17.1 40.0 -74.8 35.1
158 6 284 Class 1 Hurr GOM BF CU FL 969.3 85.1 38.5 17.1 -22.9 -79.9 29.1
160 6 167 Class 1 Hurr BF CU FL CJ 975.0 87.4 42.5 12.0 -18.1 -79.3 28.4
293,203 10,000 249 Class 3 Hurr GOM FL 941.1 116.9 32.0 10.6 90.8 -85.7 28.7
293,220 10,000 273 Class 1 Hurr NC 977.7 81.7 42.3 17.1 40.0 -74.8 35.1
Components of a catastrophe model Size of event catalogue varies by region and peril
Region Peril Years with
Events Number of
Events
Australia Earthquake 4,027 5,122
Australia Cyclone 9,954 59,377
Chile Earthquake 9,447 28,977
Europe Earthquake 10,000 176,904
Europe Winterstorm 9,985 27,557
Hawaii Earthquake 5,623 9,757
Hawaii Cyclone 2,872 3,491
Japan Earthquake 9,998 83,608
Japan Cyclone 9,995 79,885
Canada Earthquake 4,938 6,820
Canada Tornado / Hail 10,000 110,017
Columbia Earthquake 9,375 27,335
Mexico Earthquake 9,946 51,483
South East Asia Earthquake 10,000 190,406
USA Earthquake 9,853 42,765
USA Tornado / Hagel 10,000 415,838
… … … …
Currently active at
71 full models
6,388,024 events
Nat Cat Know ledge for UWs
A set of rules to calculate the intensity of
each event at the site of interest
(i.e. where the exposure sits)
Taking into account effects such as:
• EQ: ground motion attenuation, soil,
liquefaction, …
• WS: terrain roughness, surface friction,
distance to coast, …
Requires knowledge about where the
exposure is located (geocoding)
Components of a catastrophe model - Vulnerabilities Intensity calculation at the sites of Exposure
Local
intensities
Damage-
Estimation
Exposure
Database
of
Insured
Locations
Vulnerability
Components of a catastrophe model - Vulnerabilities Intensity calculation at the locations in exposure database
0
0.25
0.5
0.75
1
120 132.5 145 157.5 170 182.5 195 207.5 220 232.5 245 257.5 270
Da
ma
ge R
ati
o
Wind Speed
Building
Content
Vulnerability Functions
describe the physical impact
of an event on risks
Dependence on building
characteristics
• Construction type (Concrete,
Steel, …)
• Occupancy type (Single
Family, Office Tower, Filling
station)
• Retrofits
• Age, Size, etc
Further refinement:
Secondary uncertainty
modelling to capture
uncertainty around the mean
Vulnerability Function
The vulnerability function combines
the event intensity with the
exposure at risk
For any given event in the event
set the model is able to produce
the loss to each individual location
The size of the loss depends on
the replacement values encoded in
the exposure database
In aggregate models the exposure
is disaggregated within the zones
used using industry average
assumptions
Yields damage for given local hazard intensity
734
50,734
9,496
33,657
Damage to building $24,000
Roof blown, Water damage $65,000 material damage $10,000 content damage $4,000 additional living expenses
Damage to building $5,000
Nat Cat Know ledge for UWs
Policy conditions are provided by the
user (detailed models only)
Limits, deductibles, franchises
Multi-location policies
The financial module converts the
ground up losses into losses borne
by the policy issuer (insurer)
After aggregation across the portfolio
losses for reinsurance can be
calculated for RI treaty structures
Both of these operations can be
performed for each and every event
Runtimes from minutes to hours on
multiple CPU cores
Components of a catastrophe model Insurance Structure
Damage-
Estimation
Calculation of
insured Losses
Loss evaluation
Policy,
Treaty
conditions
Financial module
Event Year Loss
2 1 246,082
13 1 145,694
32 1 247,376
41 2 174,950
70 3 633,054
83 3 104,637
115 4 826,074
141 5 715,073
146 5 9,529,771
152 6 209,433
156 6 758,547
158 6 649,053
160 6 281,172
… … …
293,203 10000 56,900,867
293,220 10000 246,082
Event Loss Table
ELT contains losses for every modelled
event
Various loss perspectives can be
generated
• ground up without primary policies
• gross of reinsurance
• reinsurance treaty loss
• net of reinsurance
The order of the events and allocation
to the years is the same for every
model run
• allows correlation
Multiple peril models may be combined
Typical Cat Model Output
Event Loss Table Visualization for Atlantic Cyclone Model
Event Year Loss
2 1 246,082
13 1 145,694
32 1 247,376
41 2 174,950
70 3 633,054
83 3 104,637
115 4 826,074
141 5 715,073
146 5 9,529,771
152 6 209,433
156 6 758,547
158 6 649,053
160 6 281,172
… … …
293,203 10000 56,900,867
293,220 10000 246,082
Event Loss Table
Risk Measures can be derived
the from ELT
Average Annual Loss
• Sum of Loss column / # of simulation years
• Here: AAL = 4,194,198
Distribution function of
• Annual Loss (AEP)
= sum of all losses within a year
• Maximal Annual Loss (OEP)
= maximum loss occurring within a year
Typical Cat Model Output
Event Year Loss
2 1 246,082
13 1 145,694
32 1 247,376
41 2 174,950
70 3 633,054
83 3 104,637
115 4 826,074
141 5 715,073
146 5 9,529,771
152 6 209,433
156 6 758,547
158 6 649,053
160 6 281,172
… … …
293,203 10000 56,900,867
293,220 10000 246,082
NEP Year Loss
99.99% 8,699 146,288,665
99.98% 61 139,881,498
99.97% 1,009 132,765,843
99.96% 6,053 128,719,502
99.95% 9,138 112,700,537
99.94% 394 107,645,749
99.93% 1,921 103,182,358
99.92% 465 100,883,397
99.91% 62 99,363,237
99.90% 8,096 98,259,502
99.89% 9,997 96,387,580
99.88% 2,624 94,026,456
99.87% 8,709 91,529,827
99.86% 8,904 91,391,417
99.85% 7,283 87,835,321
… … …
Event Loss Table
Take the maximum loss per year and
resort the resulting table in descending
order
The nth largest value then corresponds
to the non-exceedance probability NEP
= 1 – n/10000
• Example: 10th largest loss has a 0.1%
chance of being exceeded
• Cat-Modeller lingo:
„The 1,000y event is 98m“
Similar for AEP, here losses per year
are added before re-sorting
Obtaining the OEP Distribution
Event Loss Table
0
20
40
60
80
100
120
140
160
98.0% 98.5% 99.0% 99.5%
OEP
AEP
Loss Distribution unit: millions
Insurers often base their
reinsurance buying on
VaR99.5% or similar
Regulatory requirements
are often formulated
using VaR
Typical Cat Model Output
VaR99.5%
approx. 57m (AEP)
VaR99%
approx. 35m (OEP)
Reinsurance Portfolio Management
Portfolio Management of a Reinsurer
The portfolio of a reinsurer contains thousand of treaties and is constantly changing
Treaties are
• usually underwritten on an annual basis
• shared between multiple reinsurers
Key questions when treaty is newly offered or comes up for renewal:
• How much share do I underwrite this year?
• Do I underwrite the treaty at all?
Decision needs to take into account the impact of the treaty on overall risk position
• Capital consumption
• Diversification
• Limit and Thresholds
Portfolio Analysis for a RI portfolio Event Loss Table Aggregation by Summation per Event
Event Yr Loss TTY 1
Share 5%
Loss TTY 2
Share 10%
…
Loss TTY n
Share 1%
Sum of all treaty losses
2 1 246,082 3,748,714 2,328,360 11,859,892
13 1 145,694 2,648,593 1,369,912 7,527,418
32 1 247,376 5,497,345 2,791,043 17,576,262
41 2 174,950 2,310,288 1,046,783 6,735,673
70 3 633,054 11,511,951 8,732,334 46,173,624
83 3 104,637 1,381,777 … 563,212 3,972,285
115 4 826,074 17,134,595 8,627,970 51,610,261
141 5 715,073 12,391,297 7,542,775 41,686,344
146 5 9,529,771 136,684,313 73,672,402 434,192,308
152 6 209,433 3,786,295 1,891,014 10,291,979
156 6 758,547 6,096,586 2,359,949 17,655,984
158 6 649,053 8,100,430 4,529,365 27,356,401
160 6 281,172 4,856,102 2,786,358 15,934,921
… … … … …
Portfolio Analysis for a RI portfolio Event Loss Table Aggregation by Summation per Event
Event Yr Loss TTY 1
Share 5%
Loss TTY 2
Share 10%
…
Loss TTY n
Share 1%
Loss TTY n+1 Share 2%
Sum of all treaty losses
2 1 246,082 3,748,714 2,328,360 435,685 12,295,577
13 1 145,694 2,648,593 1,369,912 147,553 7,674,971
32 1 247,376 5,497,345 2,791,043 695,146 18,271,407
41 2 174,950 2,310,288 1,046,783 205,147 6,940,820
70 3 633,054 11,511,951 8,732,334 1,022,725 47,196,349
83 3 104,637 1,381,777 … 563,212 171,701 4,143,986
115 4 826,074 17,134,595 8,627,970 1,292,664 52,902,926
141 5 715,073 12,391,297 7,542,775 1,428,358 43,114,703
146 5 9,529,771 136,684,313 73,672,402 11,688,491 445,880,799
152 6 209,433 3,786,295 1,891,014 234,117 10,526,096
156 6 758,547 6,096,586 2,359,949 781,874 18,437,858
158 6 649,053 8,100,430 4,529,365 671,004 28,027,405
160 6 281,172 4,856,102 2,786,358 569,558 16,504,478
… … … … …
The Global Reinsurer's Conundrum
For a small portfolio of XL treaties in a single county the problem is well-behaved
Impact analyses and reporting can be done with some effort in Excel
On a global basis the problem becomes much more complex:
Portfolio consists of several thousand treaties
200 countries need to be monitored for multiple perils (EQ, WS, TC, FL, FI, TH)
Dozens of underwriters are active and changing the portfolio at the same time
Choice of risk measure
Non-modelled treaty types
Sparse data
Integration with treaty management system and underwriting/modelling workflows
Global Exposure Management (GEM)
Scope / Users
Group cat business worldwide
• underwriting centres
• worldwide exposure
Underwriters: 150
Modelers/Actuaries: 30
Development / Technology
GEM Front-End: Silverlight
Reporting Database: ORACLE
BI/Reporting: MicroStrategy
Development time: 6 years
GEM Frontend
and Reporting
Real-time
Reporting
Database
Treaty Data
Cat Models
Treaty
Management
System
Exposure Data
Excel
Worksheets
Gross/Net views for
Internal Model
Challenge: 200 countries need to be monitored Focus on most important scenarios
Challenge
Global reinsurer underwrites treaties in most countries of the world
„Small“ perils can have considerable significance to the (re)insurance industry
• 2011 Thailand flood
• 2016 Canada Bushfire have
In many cases no vendor models / event sets are available to model the peril
Solution
Neglecting the perils is not an option!
Very small scenarios can be monitored with a reduced event set
• Events are suitably chosen to represent a 10, 20, …, 10000 year event in the region
For larger „second tier“ scenarios proxy models can fill the gap
Stochastics to the Rescue
General Idea
The presented approach of Nat Cat model building may be infeasible in many
secondary markets
• availability of relevant scientific data
• access to insurance coverage or claim details
Stochastic/mathematical models may be helpful in these occasions
These are informed by the sparse data that is available
Example: Winterstorm Peril in Japan
Winterstorms are regular events in Japan, esp. in the north
In rare circumstances, these can cause significant damage
E.g. 2014 February winterstorm in Japan caused 2.5 bn USD insured loss
Mathematical Models may be helpful where not nat cat models are available
Stochastics to the Rescue
Modelling the hazard as a sum of compound models
𝒀 = 𝒀𝒊
𝒏
𝒊=𝟏
, 𝑌𝑖 = 𝑌𝑖𝑘
𝑁𝑖
𝑘=0
, with 𝑌ik i.i.d. for fixed 𝑖
Japan Winterstorm Example
Japan is divided into a number of uncorrelated regions (subscenarios)
For each subscenario 𝑖 mathematical loss distributions
for the frequency 𝑁𝑖 and severity 𝑌𝑖𝑘 are chosen
These can be fitted to the available loss history, where available
If 𝑌𝑖 is expressed as a loss ratio a universal model for
aggregate exposure 𝐸𝑖 (sum insured in region 𝑖) results:
Mathematical Models may be helpful where not nat cat models are available
𝑌𝑖 = 𝐸𝑖 ∙ 𝑌𝑖𝑘
𝑁𝑖
𝑘=0
Challenge: Choice of risk measure
Challenge
Assign a suitable risk measure 𝜌(∙) to each treaty 𝑋𝑖 and the portfolio 𝑋 = 𝑋𝑖
It shall be used to measure and limit the risk contained in the overall portfolio 𝜌 𝑋
What are desirable features?
Subadditivity 𝜌 𝑋1 + 𝑋2 ≤ 𝜌 𝑋1 + 𝜌 𝑋2
+ve Homogeneity 𝜌 𝛼𝑋 = 𝛼𝜌 𝑋 , 𝛼 ≥ 0
VaR does not satisfy the first property!
• Easy to construct examples where 𝑉𝑎𝑅 𝑋1 +𝑋2 > 𝑉𝑎𝑅 𝑋1 +𝑉𝑎𝑅 𝑋2
• Also 𝑉𝑎𝑅(𝑋) may be zero while has 𝑋 a very high risk in the tail of the distribution
A better risk measure is the tail value at risk (TVaR)
VaR does not work for the ELT approach
0
20
40
60
80
100
120
140
160
98.0% 98.5% 99.0% 99.5%
Event Loss Table
Loss Distribution unit: millions
Definition TVaR
E.g. for NEP = 99%
𝑇𝑉𝑎𝑅99%(𝑋)
≔ 𝑬 𝑋| 𝑋 ≥ 𝑉𝑎𝑅99%
With ELTs the TVaR
contribution can be easily
calculated as well:
Average of worst 100 years
Advantages of TVaR over VaR:
Takes tail into account
Stability against perturbations
Subadditivity
Typical Cat Model Output
TVaR99%
approx. 65m (AEP) VaR99.5%
approx. 57m (AEP)
Event Yr Loss TTY 1
Share 5%
Loss TTY 2
Share 10%
…
Loss TTY n
Share 1% Sum of all
treaty losses
2 1 246,082 3,748,714 2,328,360 11,859,892 13 1 145,694 2,648,593 1,369,912 7,527,418 32 1 247,376 5,497,345 2,791,043 17,576,262 41 2 174,950 2,310,288 1,046,783 6,735,673 70 3 633,054 11,511,951 8,732,334 46,173,624 83 3 104,637 1,381,777 … 563,212 3,972,285
115 4 826,074 17,134,595 8,627,970 51,610,261 141 5 715,073 12,391,297 7,542,775 41,686,344 146 5 9,529,771 136,684,313 73,672,402 434,192,308 152 6 209,433 3,786,295 1,891,014 10,291,979 156 6 758,547 6,096,586 2,359,949 17,655,984 158 6 649,053 8,100,430 4,529,365 27,356,401 160 6 281,172 4,856,102 2,786,358 15,934,921
… … … … …
Portfolio analysis for a RI portfolio Correlation comes for free as it is implicit in the assignment of losses to events
NEP Year Sum of Treaty Losses per year
99.99% 1,009 7,307,159,427 99.98% 6,053 6,261,320,262 99.97% 9,138 6,153,631,045 99.96% 8,096 5,219,283,610 99.95% 7,755 4,311,811,263 99.94% 4,979 4,152,177,249 99.93% 2,624 4,119,869,853 99.92% 242 4,103,179,042 99.91% 62 4,057,460,164 99.90% 8,904 4,052,764,146 99.89% 8,056 3,977,655,473
… 99.00% 1,921 1,868,418,580
… …
aggregate
and sort
𝑇𝑉𝑎𝑅99%= 2,777,452,233
avera
ge
TVaR as Risk Measure
Recalculation of the portfolio TVaR is computationally expensive
Calculation of portfolio TVaR requires a consideration of whole event loss table
• The worst 100 years change in the sorting process
Changing a single treaty requires full recalculation to view impact on group risk
• very expensive operation
• Current event loss table size > 1bn rows
• minutes to hours on a standard ORACLE enterprise database
• could benefit from in-memory technologies
For underwriting decision making and practical purposes
𝜌 𝑋𝑖 ≔ 𝑇𝑉𝑎𝑅99% 𝑋𝑖 does not depend on 𝑋𝑗 , 𝑗 ≠ 𝑖
However, 𝜌 𝑋𝑖 should be reflective of diversification benefit of 𝑋𝑖 wrt the portfolio 𝑋
On the other hand 𝜌 𝑋𝑖 should be stable if other parts of the portfolio change
Practical Considerations
Recalculation the portfolio TVaR
Both problems can be tackled as follows
Initially the calculation of the portfolio TVaR is carried out above
Years contributing to TVaR values are fixed, say 𝑦1, 𝑦2, … , 𝑦100
Each 𝑋𝑖 is assigned the risk measure
𝜌 𝑋𝑖 ≔ average loss in the fixed simulation years 𝑦1, 𝑦2,… , 𝑦100
( instead of 𝜌 𝑋𝑖 = average loss in the worst 100 simulation years )
This is called the TVaR contribution of 𝑋𝑖
It measures the contribution of 𝑋𝑖 to the worst 100 simulation years for 𝑋
Very expensive database operation, can be simplified by switch to TVaR-Contrib
TVaR-Contrib - Advantages
Changing the risk measure to 𝜌 has many advantages
𝜌 𝑋𝑖 for a single treaty can be calculated using the treaty ELT only
Changing a treaty 𝑋𝑖 do not affect 𝜌(𝑋𝑗), 𝑖 ≠ 𝑗
TVaR-contrib is additive and homogeneous:
𝜌 𝑋 = 𝜌 ( 𝑋𝑖) = 𝜌 (𝑋𝑖) important for segmentation
𝜌 𝛼𝑋𝑖 = 𝛼𝜌 𝑋𝑖 treaty share can be calculated directly
It allows very efficient reporting and as-if / impact analysis
In practice, for a large reinsurer: 𝜌 𝑋 ≈ 𝜌 𝑋
• even after busy renewal seasons with lots of portfolio changes
• quarterly re-calculation of 𝑦1, 𝑦2, … , 𝑦100
TVaR-Contrib is good and stable approximation of TVaR
Sample Report for a Single Treaty for UW Decision Making Reports are available immediately after data is entered
Scenario
TVaR
Contribution 𝝆 for
10% share (Capacity consumption)
Remaing
Capacity for UW
Center
Group
Capacity
Atlantic Hurricane 2.3 37.0 1,234.4
US Earthquake 9.8 5.3 1,345.1
Europe Winterstorm 18.2 20.4 912.9
Europe Earthquake 5.0 6.0 423.7
Japan Earthquake 2.1 2.7 934.7
Australia Cyclone 5.2 11.5 545.4
Australia Earthquake 4.5 68.5 456.1
…
all values in mn, f ictional data
Challenge: Non-Modelled Treaties
Challenge
A considerable portion of business may not be modelled using cat models
• nature of the business (marine, personal accident, etc.)
• lack of exposure data
To obtain comprehensive view on risk this business also needs consideration
Solutions
RDS scenarios help identifying those treaties
Bespoke encoding functionalities are available in the GEM Front End
• Models for certain classes of business (Exposure data wizards)
− Per Risk XL, Marine, Worker‘s Compensation
• Third party model results for models not directly connected to GEM
• Last resort: Loss Estimations
Output: Event loss tables stored in the reporting database
Beyond Probabilities
Idea
Stress test a company by as-if analysis of a specific event
Breadth rather than depth: Uncover hidden pockets of exposure
Procedure
Prescribe / describe a hypothetical
set of catastrophe events
Events should be of considerable magnitude,
and well described
Gather potential losses from all involved
underwriting departments, even those with
remote exposures
Consider unexpected sources of loss
Standard approach in the Lloyd‘s market
Other methods of risk measurement - Realistic Desaster Scenarios (RDS)
Extrapolation Methods Used to Map Losses to Events Simplest Case: Estimation of the 100 Year Event
0
10,000
20,000
30,000
40,000
50,000
60,000
98.0% 98.5% 99.0% 99.5%
Market Losses (mn)
User Input
Treaty Type Quota Share
Event Limit 150,000,000
Estimated 100y loss 120,000,000
0
20
40
60
80
100
120
140
160
98.0% 98.5% 99.0% 99.5%
Treaty Loss (mn)
Event Treaty Loss
2 1,203
13 125,050
32 100,332
… …
Integration with TMS and workflows
Challenge
The system needs essential treaty and exposure data in real time
during very busy renewal times to produce sensible output for decision makers
Solutions
System only captures data essential for the modeling / underwriting process
• Renewal functionalities make it easy to work off last year‘s data
Full integration with
• treaty management system
• underwriting worksheets
• modelling worksheets
Data needs to be keyed in only once (not thrice)
Modelling / Quotation process fully supported
• Consistency between pricing and accumulation control
Seamless interfaces ensure smooth workflow and limited extra efforts
GEM Frontend
and Reporting
Treaty
Management
System
Excel
Worksheets
Real-time Exposure Management for a Reinsurer
„Big data Vs“
Volume: Thousands of treaties, each generates 100,000s
rows of data, but data used for reporting is structured
Velocity Portfolio is constantly changing, reporting in real-time
Variety Exposure data comes from different unstructured and structured
sources; peril/region specific data/models; various treaty types
However, data only enters the system in structured form
Veracity Input data and model output may be sparse, unreliable or both
While the described system meets some of the criteria it can be considered a
Business Intelligence rather than a Big Data system.
But there is no doubt about the fifth V!
Big Data!?
Real-time Exposure Management for a Reinsurer
Value!
GEM has automatized many process steps which required onerous manual
interaction before
Great improvements to speed and quality of risk management reports
GEM has become invaluable in underwriting decision making
Underwriting close to assigned limits
Optimal use of capital
• Immediate reactions to external market disruptions are possible
• Large cat events; Disruptions to the capital markets
Timely and granular data delivery to the internal model
The Fifth V