nat cat-risikomanagement in echtzeit - aktuar

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


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


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


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


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


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


„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!?


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

nat cat-risikomanagement in echtzeit - aktuar

Documents