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Introduction to

Catastrophe Modeling

Claims Club Asia

Hong Kong, 23rd May 2013

1

Agenda

Basics of Catastrophe Modeling

Cat Modeling Outputs

Uses of Cat Models

Important Considerations

2

Basics of Catastrophe Modeling

3

What are Catastrophes?

Low frequency, extreme events, causes severe losses

Limited historical data

Normally related to natural disasters, such as Typhoon, Earthquake and

Flood

Can also describe concentrated or widespread damage from man-made

disasters, e.g. Terrorism, epidemics

4

11.03.2011 Japan EQ & Tsunami

Source: earthquakejapan2011

Tsunami

Source: Alertnet

Fire

Source: earthquakejapan2011

Landslide

Source: earthquakejapan2011

Flood

Source: earthquakejapan2011

Liquefaction

EQ

Nuclear

Source: Digitalglobe

5

What are Catastrophe Models?

Catastrophe Models (Cat Models) are simulation models based on

– Science of Peril

– Historical and Pre-Historical Data on Magnitude and Frequency of Peril

– Expert Knowledge

– Engineering Knowledge of Damageability

– Historical Loss Data and

– Insurance Policy Terms

For estimating

– Magnitude and Occurrence Rate of losses in events that are likely over a

long simulation period

6

Development of Cat Models

Prior to the advent of cat models, industry’s usual approach was to estimate

the Max % of Total Insured Value in an area that might suffer loss from a

realistic event, either based on past experience or expert’s judgment

Source: Nat Cat Risk Management from (Re) insurers perspectives

(2011) Dr He Hua, 9th Conference on Catastrophe Insurance in Asia.

7

Development of Cat Models (cont’d)

Other shortcomings of traditional ratemaking methods for Cat:

– Assumes Cat activities as “normal”

– Assumes population demographics were stable

– Assumes insured losses by peril were stable

– Does not take into account changes in building/construction codes

The introduction of fully probabilistic models represented a major step

forward by providing a scientific basis for assessing both the frequency and

severity of catastrophe risks.

When introduced, the use of catastrophe models was not widespread. Two

disasters in 1989 (Hurricane Hugo and the Loma Prieta Earthquake) sent a

warning signal to the insurance industry.

Catastrophe models gained rapid acceptance in the insurance and

reinsurance industries after Hurricane Andrew devastated parts of Miami in

1992, causing the largest insured loss experienced worldwide at that time.

Source: Issues in the Regulatory Acceptance of Computer Modeling in Property Insurance

Ratemaking (Rade T Musulin)

8

Catastrophe Model Vendors

AIR – (Applied Insurance Research); Founded in 1987;

Wholly-owned subsidiary of Insurance Services Office, Inc.

(ISO).

EQE – Founded in 1994; Affiliate of ABS Consulting

RMS – (Risk Management Solutions); Founded in 1988.

IF – (Impact Forecasting); Wholly-owned Aon subsidiary.

Non-black box model that can customize damage curves to

unique exposures.

9

Best Practice Modelling Suite

Class Peril Modelling Technique AB

Property

Per Risk Exposure

Quake Catastrophe

Cyclone Catastrophe

Storm Catastrophe

Bushfire Catastrophe

Hail Catastrophe

Flood Catastrophe

Tsunami Deterministic

Other Experience

Terrorism Catastrophe

Liability

Attritional Experience

Large Loss Exposure

Mega Loss Deterministic

Clash Exposure

Cat - Bushfire Catastrophe

PIDO Exposure

Motor

Own Damage Experience

TPPD Experience

CTP Exposure

Mega Loss Exposure

Marine

Cat – Cyclone Catastrophe

Cat – Quake Catastrophe

Attritional – Cargo Experience

Attritional - Hull Experience

Liability Experience

Agriculture

Attritional Experience

Cat - Cyclone Catastrophe

Cat - Fire Experience

Cat - Storm Experience

Global Model Builders

RMS

AIR

EQECAT

Bespoke Models

Aon Benfield Analytics

Impact

Forecasting

Consultants

10

Catastrophe Modelling Components

RISK = f ( hazard, exposure, vulnerability)

Exposure

data

Determine earthquake

motion and wind speed

Hazard Module

Calculate damage

Vulnerability Module

Quantify financial loss

Financial Module EP

Curve

11

Risk Data

Lo

ca

tio

n

Province

County

Postal Code

Street Address

Lat/Long

Att

rib

ute

Occupancy

Construction Type

Building Height

Year Build

Poly terms i.e. Limits, Deductible

Risk

12

Data Flow of Cat Model

Intensity of Hazard at Site

Building

Characteristics

Ground up

Loss

Policy Terms

Gross

Loss

Location of

Site

Parameters That Define Event

Including Magnitude, Physical

Location, Rate

Damage Curve

Items in light blue boxes are

user specified

Local

Conditions at

Site

Event Set

Industry Building

Inventory

Portfolio Vulnerability

Reinsurance

Structure

Net Loss

Ceded

Loss

13

Cat Modeling Outputs

14

Cat Modeling Outputs – Event Loss Tables

An Event Loss Table (ELT) is a table that contains for each event, the event

id, the annual rate of occurrence of the event, the expected loss caused by

the event, the affected exposure, and the uncertainty around the expected

loss as expressed by the standard deviation of the loss

15

Cat Modeling Outputs – The EP Curve

The OEP curve deals with individual

occurrences in a year. It shows the annual

probability that the losses for at least one

occurrence will exceed a certain amount. The

OEP curve is also known as the maximum

loss distribution.

The AEP curve deals with aggregate loss

dollars in a one-year time period. It shows the

probability that aggregate losses in a year

(i.e. the sum of all losses from all occurrences

in a year) will be greater than a certain

amount.

The AAL (Annual Average Loss) is the area

under the AEP curve. It is also known as Pure

Premium.

EP curves are cumulative distributions showing the probability that losses will exceed a certain

amount, from either single or multiple occurrences. These losses are expressed in the

Occurrence Exceedance Probability (OEP) and the Aggregate Exceedance Probability

(AEP) curves.

16

Uses of Catastrophe Models

17

Catastrophe Model Uses

Assess the risk in a portfolio of exposures:

– Total exposure and capital requirements

• Internal ERM, regulatory and rating agencies’

– Underwriting

– Primary pricing

– Aggregate management

– Reinsurance decisions

• Structure and pricing

• Alternative solutions

18

Total Exposure & Capital Requirements

0.00

5.00

10.00

15.00

20.00

25.00

30.00

0 100 200 300 400 500 600 700 800 900 1000

Mo

de

lled

Lo

sse

s (P

AK

Ru

pe

es B

illio

n)

Return Period (Years)

Net Retained - Without Event Limit

Earthquake

Typhoon

Combined Perils

Return Period Earthquake Typhoon Combined Perils

1000 23.12 15.48 24.64

500 17.40 12.85 19.28

250 12.12 10.01 14.66

100 6.06 6.57 9.67

50 2.75 4.10 6.38

20 0.79 1.60 2.82

10 0.22 0.44 1.15

Mean Loss 0.26 0.31 0.57

Std Dev 1.63 1.35 2.11

Modelled SI 406.25 410.83

250 Yr % of SI 3.0% 2.4%

100 Yr % of SI 1.5% 1.6%

Important note: A 1-in-250 Year event ≠ it only happens once

every 250 years. Instead, it means there is a 0.4% chance of the

event happening in any given year.

19

Regulatory/Rating Agency Requirements

Capital Model Return Period/Peril Basis

Australia 1:250 – All Perils Occurrence

Bermuda 1:100 TVaR – All Perils Aggregate

Canada 1:370 - Earthquake Occurrence

Japan

Greater of:

1:250 - Earthquake Occurrence

1:70 - Wind Occurrence

Lloyd’s RDS an 1-in-200 year all risk estimate within the ICA Aggregate

Solvency I None

Solvency II 1:200 – All Perils Aggregate

UK None for ECR; however ICA includes a 1-in-200 year all risk estimate

US None

AM Best BCar

Greater of:

1:250 - Earthquake Occurrence

1:200 - Wind Occurrence

S&P Enhanced 1:250 – All Perils Aggregate

20

Aggregate Management

21

Example Peak Zones 1 in 250 Year Event Loss

42

22

Underwriting

23

Pricing Cat Risk

Total Cat Cost = Net AAL + ( Reinsurance Premium ) + Net Capital Cost

= Net AAL + (Ceded AAL + Reinsurance Margin) + Net Capital Cost

= (Net AAL + Ceded AAL) + Reinsurance Margin + Net Capital Cost

= Gross AAL + Reinsurance Margin + Net Capital Cost

Model Output + Client

Model Miss

Allocation driven by

Ceded AAL

Volatility

Correlation to

industry

Allocation driven by

Volatility

Correlation to

portfolio

24

Reinsurance Design, Pricing & Cost Allocation

XYZ's Non Marine Excess of Loss Program

100m xs 200m

CNY 200m

80m xs 120m

CNY 120m

60m xs 60m

CNY 60m

30m xs 30m

CNY 30m

15m xs 15m

CNY 15m

0

50

100

150

200

250

300

0 200 400 600 800 1,000

Mo

de

lle

d L

os

s (

Millio

ns

)

Return Period (Years)

RMS EQ AIR TY

25

Reinsurance Design, Pricing & Cost Allocation

XYZ's Non Marine Excess of Loss Program

100m xs 200m

CNY 200m

80m xs 120m

CNY 120m

60m xs 60m

CNY 60m

30m xs 30m

CNY 30m

15m xs 15m

CNY 15m

Layer 1 Beijing Guangdong Guangxi Hebei Tianjin

Expected Loss 63 135 15 51 63

Standard Deviation 88.2 189 18.6 71.4 78.12

Layer 3 Beijing Guangdong Guangxi Hebei Tianjin

Expected Loss 110.5 97.75 61.2 75.65 97.75

Standard Deviation 154.7 136.85 75.888 105.91 121.21

Layer 5 Beijing Guangdong Guangxi Hebei Tianjin

Expected Loss 140 101 0 0 123

Standard Deviation 224 131.3 0 0 196.8

26

Important Considerations

27

Important Considerations when Pricing Cat

Cat Models DO NOT PREDICT future Cat losses

Important Considerations

– Model Misses

– Data Quality

– Demographic & Economic Changes

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Model Misses

• Noticed during benchmarking of

individual events - Difference between

actual and modelled loss

• No cat model is able to fully reproduce a

historical loss

• Models differ from reality

• Distribution of possible damage not a point

loss

• Law of Large Numbers applies – run

enough events and on average the cat

model will converge towards reality

Modeled Loss

Actual Loss

Demand surge

Risk

Concentrations

Model

inaccuracies

-hazard

-vulnerability

Unmodelled Perils

Missing or

undervalued

exposures

Modelling

assumptions

29

Allow for Model Miss – Load Cat Model Results

• Unmodelled perils

• Fire following earthquake (FFE),

Tsunami

• FFE – one vendor advised 20% -

could be a lot higher

• Risk concentrations

• Cat models assume geographic

spread of risk with limited correlation

of loss between risks

• High correlation of loss in tight

clusters however

• Likely to under-estimate

• Demand surge

• Could be as high as 30%

30

Data Quality - Importance of Exposure Data

Enough emphasis cannot be put on the exposure

data; it in the end will determine the quality of the

catastrophe model.

"All discussions of catastrophic exposure

management begin with the accuracy and availability

of the exposure data. The most sophisticated,

complex catastrophe modeling systems cannot

estimate an insurer’s losses if the insurer cannot

identify what insurance coverages have been written

and where those risks are located.”

Source: Measuring and Managing Catastrophe Risk (1995)

Kozlowski &Mathewson, CAS.

31

Data Quality – The Need for Building Attributes

• Occupancy

• Construction

• Building Height

• Building Age • Varying building codes

• Policy Terms • Deductible

• Limit

• Number of Risk

• 。。。

32

Economic and Demographic Changes

Source: The World in 2050, PriceWaterhouseCoopers, 2006

0

5

10

15

20

25

30

US$

Trill

ion

2009

2050

Real GDP (2009 and 2050)

33

Rapid Development – Much of It in Risk Zones

Source: 快速城市化地区土地利用变化的水文响应模拟研究, Jing Zheng 2007

34

Summary: Economic and Demographic Changes

Images to the left are Shanghai Pudong, 1990

vs. today

35

Thank You!

Carole Ho FCAS | Executive Director

Aon Benfield China Limited | Aon Benfield Analytics

t +85228624183 | f +85222438924

carole.ho@aonbenfield.com