CAT Loss Modeling and Analytics - PowerPoint Presentation

Slide1

CAT Loss Modeling and AnalyticsMarch 20, 2015

Prepared for:

Society for Risk Management ConsultantsSlide2

passion. innovation. accountability.

Introduction to Beecher Carlson

>Slide3

Who we are

3

ABOUT US

Beecher Carlson is a large account broker and risk management consultant that delivers expertise through industry focus and product specialization. By leveraging our deep risk management expertise, we are able to help clients manage their business risks, protect and enhance their capital and fulfill their corporate mission. Beecher Carlson is a subsidiary of Brown & Brown, Inc. and is headquartered in Atlanta, GA. Brown & Brown, based in Daytona Beach, FL, is one of the nation’s leading independent insurance intermediaries and is ranked as the sixth largest insurance brokerage in the United States and the seventh largest brokerage worldwide by

Business Insurance

magazine.

PREMIUM

VOLUME

In 2013, Beecher Carlson and Brown & Brown collectively placed

more than $12.3 Billion

in premium volume.

$12.3B

REVENUE

In 2014, Brown & Brown revenue reached $1,567,460,000.

$1.6B

DEDICATED

At Beecher Carlson, more than 40% of our corporate expense is dedicated to claims, risk control and analytics.

40%

SPECIALIZED INDUSTRIES

Hospitality

Real Estate

Manufacturing

Healthcare

Energy

Financial ServicesTechnology

7

RECOGNIZED

Ranked #1 as surveyed by Greenwich Associates

with over 700 risk managers for Favorability based on the key attributes of: Ethicality, Flexibility, Likelihood to Recommend, Client Satisfaction, Prompt Follow-up, Addressing Policy Issues with Adequate Time, Compensation & Pricing, Innovativeness of Brokerage.

#1Slide4

Specialized Insurance Brokerage

ENERGY

FINANCIAL SERVICES

HEALTHCARE

HOSPITALITY

MANUFACTURING

REAL ESTATE

We specialize in specific industry verticals, and our brokerage teams further focus on specific product lines within those industries.

Specialization has given Beecher Carlson true expertise in our markets, allowing us to offer differentiated, customized and value–added client solutions

.

4

TechnologySlide5

Business Insurance Rankings5

July 21, 2014

www.business insurance.com

SPECIAL REPORT

100 LARGEST BROKERS

Ranked by 2013 brokerage revenue

2014

rank

2013

rank

Company

2013

U.S.

broker revenue

%

change

1

1

Aon P.L.C.

$5,561,106,600

4.6%

2

2

Marsh &

McLennan Cos. Inc.

1

$5,521,500,0005.2%

33

Arthur J. Gallagher & Co.1$2,111,340,00010.7%44Willis Group Holdings P.L.C.

$1,743,840,0007.3%

5

6

BB&T Insurance Holdings, Inc.

1

$1,582,443,400

6.9%

6

7

Brown & Brown Inc.

$1,355,502,535

14.0%**

7

5

Wells Fargo

Insurance Services USA Inc.

$1,350,022,000

(14.3%)

8

8

Lockton Cos. LLC

2

$826,448,280

12.4%**

9

10

USI Holdings Corp.

1

$782,207,827

9.8%

10

11

Hub International

Ltd.

1

$768,865,200

21.5%**

Beecher Carlson is the large accounts retail broker entity of Brown & Brown, the 6

th

largest U.S. insurance broker firm.Slide6

passion. innovation. accountability.

Agenda

>Slide7

What We Will be Discussing

A Historical Review of CATs

Overview of CAT Models

Terminology – Key Terms

Data and Data Formats

Uses and Users of CAT Models

Components of CAT Models

Data Quality

Secondary Modifiers

A Closer Look at 20 Secondary EQ Modifiers

- Two case studies

What’s Next?

For those that want to know more:

Appendix 1: CAT Modeling Terminology

Appendix 2: Secondary Modifier Tables

Appendix 3: Wind/ Storm Surge Secondary Modifier: Roof AnchorsAppendix 4: Frequently Asked Questions

Appendix 5: Sources

This will be the focus of today’s discussion

7Slide8

passion. innovation. accountability.

A Historical Review of CATs

>Slide9

Swiss Re, 2011:The term “natural catastrophe” refers to an event caused by natural forces. Such an event generally results in a large number of individual losses

involving

many insurance policies

. The scale of the losses resulting from a catastrophe depends not only on the

severity of the natural forces concerned, but also on man-made factors, such as

building design or the efficiency of disaster control in the affected region. Natural catastrophes are subdivided into the following categories: Flood - Cold Wavers/FrostStorm - HailEarthquake - Tsunami

Droughts/Forest Fires/Heat

Waves

- and

other natural catastrophes.

Definition of

NatCAT

events

9Slide10

While the

number

of

CAT events is steadily increasing…

10Slide11

…the total amount of both overall and insured losses has been declining in recent years

11Slide12

Eight of the ten

most costly insurance losses

world-wide have occurred during the last 15 years (original values)

Insured Losses are getting larger

12Slide13

Recent HeadlinesMarch 14,

2015 –

AccuWeather

Deadly Cyclone Pam Leaves Vanuatu, Targets New Zealand

March 12, 2015 – Business InsuranceGiant quake, tsunami risks identifiedMore than 20 subduction zones could produce giant earthquakes and tsunamis such as those that devastated the Tohoku, Japan, area in 2011, according to a tsunami risk study released by Risk Management Solutions Inc

.March 12, 2015 – Advisen FPN'Reawakened

' faults could trigger big Okla.

Earthquakes

Long-dormant, 300- million-year-old fault lines across Oklahoma are being "reawakened" by recent small earthquakes that have been previously linked to fracking, scientists reported in a new study out this week

.

March 9, 2015 – Los Angeles Times

Risk of 8.0 earthquake in California rises, USGS says

Estimates of the chance of a magnitude 8.0 or greater earthquake hitting California in the next three decades have been raised from about 4.7% to 7%, the U.S. Geological Survey said Tuesday.February 19,

2015 – ReutersAustralia's northeast braces for double cyclone hitThere is always a risk of Nat CAT events

13Slide14

Hurricane HistoryFrom 1949 in the Pacific From 1851 in the Atlantic

Source: NOAA / NWS

There is always a risk of Nat CAT events

14Slide15

passion. innovation. accountability.

Overview of CAT Models

>Slide16

A CAT model is a computerized system that generates a robust set of simulated

events

and estimates the magnitude, intensity, and location of the event to determine the

amount of damage and calculate the insured loss as a result of a catastrophic event such as a hurricane or an earthquake.

Modeled Nat CAT perils

include– Hurricane (incl. storm surge)– Earthquake (incl. fire following and EQSL)– Tornado/Hail – Winter Storm– Flood– Brushfire– Others

Sample: RMS

What is Catastrophe Modeling?

16Slide17

Why Are Catastrophe Models Run?Management of Exposures– Control

writings in regions

–

Scenario testing– Capital Costs– Probability of Ruin– Reinsurance Buying

– Rating Agency Needs– Determining Limits Needed

Ratemaking– Primary Insurers– ReinsurersUsers of Catastrophe Models

Underwriters

Reinsurers

(Re-)Insurance Brokers

Capital Market (pricing of Cat Bonds)

Regulators (solvency requirements)

Rating Agencies (S&P, A.M. Best)

Insurance Buyers

Purpose and Users of Catastrophe Modeling17Slide18

History of NatCAT Vendors Models

Property Insurance

Mapping the risk on a

wall-hung map 1800 – 1960

Development of Geographic Information SystemsNatural Hazard Science

Understanding nature and impact of natural hazards (measuring hazard intensity)

1800 seismograph, anemometer

1970 study about the frequency of NatCat events

Computer-based models

Provide estimates of NatCAT losses by overlapping the property at risk with the potential natural hazard sources in the geographical area

AIR (1987); RMS (1988); EQE (1994)

September 21, 1989 – Hurricane Hugo, $4bn insurance loss (South Carolina)

October 17, 1989 – Earthquake Loma Prieta, $6bn insurance loss (San Francisco)August 1992 – Hurricane Andrew, $15.5bn loss (Florida), AIR $13bn (9 insurance companies become insolvent)

 Need to estimate NatCAT risk more precisely1997 – HAZUS – open source FEMA model to assess EQ Risk in US2004 – HAZUS-MH – included Wind and Flood

18Slide19

Main CAT Model Vendors

–

Risk Management Solutions (RMS

), 1988 at Stanford University

Market Share Leader with reinsurers

UnavoidableRMS(one) – new platform in limited release, wider use 2015New flood model will cover US in Fall 2015 into 2016Model updates (addressing building code upgrades)–

AIR Worldwide (AIR

), 1987 in Boston

Strong and growing presence

Some technical advantages specific to public entities

Better code choices for tanks, other structures

Touchstone released in January 2013, update in 2014

Released US flood model

– EQECAT, 1994 EQE International, then ABS ConsultingRecently purchased by CoreLogicBroker ModelsCompany Proprietary Models – FM Global, Swiss Re, Munich Re, and others

Open Systems – Oasis

Choices of Models

19Slide20

Hazard – Stochastic events are simulated against the exposures. Each event has an associated probability.

Exposures

– Models start with the exposure distribution (geography, construction, occupancy, etc.).

Vulnerability

– This is the amount of damage expected to result from an event based on the exposure characteristics and event intensity.

Financial

Perspectives

– Finally, varying perspectives of the loss

are generated

(application of primary insurance conditions and

facultative and treaty reinsurance).

How CAT Models Work4 Modules

20Slide21

CAT Models are used to answer these questions:How much limit is needed?

100/250/500 etc. Year “PMLs” (Return Period)

How much

exposure will be

retained (deductibles)

How should the insurance program be layered?What is the estimated loss cost for a given layer?Average Annual Loss (AAL) by layer, including the retained deductible

Which locations are the

biggest drivers

of the modeled loss estimates?

Would better data reduce the loss estimates?

Should this information be used for premium allocation within a portfolio?

How CAT Models Work

Asking for the 1 in 250

return period is asking for the monetary loss in the range of outcomes where only 1/250 = 0.4% of potential outcomes are worse. In mathematical terms this is the 1 – 0.4% = 99.6% confidence point, and you are stating that you are ‘99.6% confident’ that losses will not be larger than this value.

21Slide22

InputInsured’s data:

including location data, exposure characteristics, and values

Coverage terms:

Policy deductibles, sublimits, layers

Settings (storm surge, demand surge, EQSL included?)

Proprietary catalogues of theoretical events:Storm path/ EQ epicenter, severity, probabilityDifferent sets may be selected, including specific eventsDamageability functions:For occupancy types

For building characteristics

How CAT Models Work

22Slide23

ProcessingCursory data quality checks“Will it run?”; “Does it make sense?”

Geocoding engine

Separate module to transform street address into coordinate data:

6-digit decimal latitude/longitude

Module bypassed if latitude/ longitude provided by user

Calculation enginePower users make big investments in technology to stay on the cutting edge for computing speedModeling portfolios used to take days, then hours, now minutesRunning several scenarios is easy: e.g. including or excluding certain locations to ascertain the change in risk after acquisitions or divestitures

How CAT Models Work

23Slide24

OutputSummary of expected loss estimatesRanked list of theoretical events,

estimate loss, probability

“PMLs” 50/100/250/500 etc. return periods – cumulative probability thresholds

Average Annual Loss (AAL) – sum of estimates x probability

Ranked list of policies/ locations (highest AAL)Basic Data quality metricsMarginal portfolio impact analysis

How CAT Models Work

24

Output depends on whether a

portfolio of locations

is modeled or whether a

portfolio of policies

covering a multitude of locations is modeled.Slide25

passion. innovation. accountability.

Terminology – Key Terms

>Slide26

Storm Surge (SS) – Quickly rising ocean water levels associated with windstorms that can cause widespread flooding. Measured as

the difference

between the predicted astronomical tide and the actual

height of the tide when it arrives. Caused by the lower barometric pressure associated with tropical or extra-tropical cyclones, and the action of the wind in piling up the surface of the water. The amount of surge depends on

a storm's strength, the path it is following, and the contours of the ocean and bay bottoms as well as the land that will be flooded.

Tornado/Hail (TH) – Non hurricane wind eventsTerminology

Earthquake

Shake (EQ)

– A sudden or abrupt movement along a fault

or other

pre-existing zone of weakness in response to accumulated stresses.

Fire

Following Earthquake (FFEQ) – Hazard presented by fires which commonly occur following an earthquake, typically due to the rupture of natural gas lines or other structures carrying combustible materials

.Earthquake Sprinkler Leakage (EQSL) – Direct damage to the building or contents caused by the leakage or discharge of water or other substances from an automatic sprinkler system due to earthquake or volcanic action.

Demand surge/Loss amplification (DS) – Post event inflation.– Shortages of labor and materials cause prices to rise.

– Supply/demand imbalances delay repairs resulting in structural deterioration.– Faced with the magnitude of the disaster and under pressure from politicians, insurers are encouraged to settle claims generously and to expand the terms of coverage

beyond those strictly defined in contracts.26Slide27

Terminology27

Exceedance Probability (EP)

–

Also known as “exceeding probability” or “EP”,

is

the probability of exceeding specified loss thresholds.EP curve defines the probability of various levels of potential loss for a defined structure or portfolio of assets at risk of loss from natural hazards.

By combining probabilities of occurrence with the loss levels of all potential events, the

probability of exceeding certain loss levels in a given year

(return period loss) can be calculated.

Expected Annual Loss (Average Annual

Loss, AAL

)

or Pure Premium

– Sum of all modeled event losses divided by the number of years modeled. This is the annual premium required to cover the loss exposure over time.The expected annual loss cost rate load is a good index of relative risk between programs and accounts. Loss cost rate loads can be developed by dividing the expected annual loss by the sums insured per hundred.Slide28

Terminology28

•

The Standard Deviation (SD)

is a dollar measure of the deviation of the potential losses away from the mean Average Annual Loss (AAL) for a layer. • The Co-efficient of Variation (

CoV, CV) represents the proportional deviation from the AAL and is calculated by SD/AAL.

The proportional nature of the CoV means it can be compared across layers to identify the level of loss uncertainty for different layer structures where a lower CoV indicates lower loss uncertainty.

= low uncertainty

= high uncertaintySlide29

Terminology29

The way to present the findings on the 500 year event line is:

“

There is a .2% probability in any 1 year that the insured will suffer a single loss exceeding the dollar amount shown in the RMS analysis.” 

Another way to say it is: “There is a .2% probability in any 1 year that the insured can expect at least one event to occur that will cause at least the dollar amount shown in the RMS 500 year analysis, of ground up loss

.”A third way to say it is: “There is a 99.8% chance in any 1 year that the insured won’t have an event that exceeds the dollar amount shown in the RMS 500 year analysis.”

If you want to look at a longer time window than a year – say 50 years - as many lending institutions do - multiply .2% by 50 years - this equals 10%. Then you can say,

“

There is a 10% probability that

during the next 50 years

the insured will suffer a loss exceeding the dollar amount shown in the RMS analysis for a 500 year event.”

This

represents a 90% confidence level that a single loss won’t exceed that amount for a 500 year event during the next 50 years.

Return Periods and Probability Slide30

passion. innovation. accountability.

Data and Data Formats

>Slide31

Raw detailed data– Format differs by model – Format into model(s) you want to use (RMS,

AIR,

EQECAT

etc.)EDM – detailed data in RiskLink format

(RMS product)UNICEDE file – aggregated data in

AIR formatUNICEDE/2 file – aggregated data in AIR formatUNICEDE/px (UPX)– detailed data in AIR format

Common Data Formats

Overview

31

Data collected but not coded = missed opportunitySlide32

Raw Data – Basic DataAddress – state, county, city, zip code, and street address

Construction

Occupancy

Values by coverage - building, contents, time elementLimits

DeductiblesPeril

specific deductibles and/or sub-limitsYear builtNumber of storiesBuilding sprinklered / non-sprinkleredNot required, but good to have - Secondary CharacteristicsIt can make a huge difference and the results!

It all begins with good Raw Data

“No COPE – No hope”

32Slide33

Report values by building, rather than by site or location. It all begins with good Raw Data

33

Report all structuresSlide34

passion. innovation. accountability.

Uses and Users of CAT Models

>Slide35

CAT Modeling - Impacts on Program Pricing, Capacity, and StructureModeling is all about the data. Models are sophisticated, but depend on the information given to them. For example, differences in how buildings in a similar location are constructed may respond

to the

same event differently (e.g., a brick building may fare better in a windstorm than one made

of wood).

Models

are capable of developing loss levels for a range of building types, ages, sizes, and occupancies.

Uses and Users of CAT Models

Modeling helps answer a variety of questions:

35Slide36

Uses and Users of CAT ModelsA valuable tool for Insurers

• Risk pricing

Using

local software, a quick repeatable risk assessment over the known locations of a risk being offered can be run. In addition to supporting the calculation of a robust internal technical price, the

CAT modeling process can provide a wealth of additional information regarding the potential hazard exposure. This includes: susceptibility to hurricanes or earthquakes,

proximity to liquefaction (the process of something moving from a solid to liquid mass) storm surge risks such as flooding, or vulnerability assessments (for example, which building standard code was in place when the locations were built and what relative impact this could have). While

there is still a lot of uncertainty and complexity to assessing such risks, the

bench-mark

figures produced allow

relative comparisons between risks

, and over time. All of this is intended to supplement an

underwriter’s wider knowledge about a risk and lead to optimal decision making over the long term, including calculating the correct price.

36Slide37

Uses and Users of CAT

Models

•

Portfolio management

As above for individual risks, so for an entire portfolio of risks, CAT modeling is used to rapidly accumulate across a portfolio to communicate the combined profile. For example, acting as a common currency, CAT modeling

can put a high value industrial facility in the US in direct comparison to a warehouse in Belgium. At this level CAT modeling supports business strategy, both identifying areas of concern (such as with too great an accumulation of correlating risks) or identifying opportunities (where diversifying risks could be added to the portfolio with marginal impact). • Capital requirements The robust standardized approach to assessing CAT

risk that

CAT modeling

provides, can benefit other processes undertaken by insurers. The main usage is in

calculating solvency

and

other regulatory or economic capital requirements, where the output from a CAT modeling process will provide a risk profile that can be combined with other forms of business risk to inform capital requirements.

37Slide38

Uses and Users of CAT Models

A valuable tool for

Insurance

BuyersInsureds often use CAT models to guide them as to

what sublimits they should buy for hurricane (windstorm) and earthquake exposures. Typically, insureds look to buy to the 1-in-250-year return period, which is the generally accepted return period. More conservative returns of 500 or

1,000 years also can be used.All models produce data in the form of tables. The RMS table (illustrated on the next slide) helps clients to understand what the expected losses may be from various CAT events; thus, helping insureds

or insurers

set acceptable program sublimits

.

38Slide39

Loss Summary: Post Deductible LossIn this example, the expected loss from earthquake and hurricane for the 250-year return period

is approximately $134.7 million and $8.5 million, respectively.

We

typically focus on

the

aggregate exceedance probability (AEP) versus the occurrence exceedance probability (OEP). The AEP is the probability that the associated loss level will be exceeded by the aggregated losses in any given year, and is used when the insurance program is written on an aggregate basis.

The OEP

is the probability that the associated loss level will be exceeded by any event in any given year

. It

is used when the insurance program is written on an occurrence basis, or when the

loss associated

with one event is important.

Uses and Users of CAT Models

39Slide40

Uses and Users of CAT Models

A valuable tool for

Insurance Brokers

Insurance brokers use the modeling results to help design the program structure, as modeling

can be performed on each individual layer as well as the overall program. This allows brokers to analyze various options

, such as insureds self insuring layers that may be too costly or transferring risk to various insurers where they see value and efficiency in so doing.Additionally, modeling allows brokers to look at annual average loss (AAL) figures, which are the minimum annual charge (premium) over an infinite time period that would need to be collected to fund for the expected loss. This is often referred to as the “technical premium.” Carriers often use

a multiple

of this figure to determine the actual annual premium charged. Accordingly, comparing

a company’s

AAL for earthquake and windstorm perils versus the actual premium paid can help

clients determine

how well priced (or not) their program is overall.

40Slide41

41

Modeling should be considered a best practice and “baked into” any buying / renewal strategy

Know “why you buy” what you buy and how

CAT modeling impacts program pricing,

capacity

and structure

Uses and Users of CAT ModelsSlide42

Uses and Users of CAT Models

Models are just one of many tools

It

is important for re/insurers to remember

that catastrophe

models are just one tool that an underwriter has at his or her disposal when analyzing a policy or portfolio. While a model's stochastic event data set is designed to simulate all events that could take place,

a storm

, flood or earthquake with

characteristics that

are not contemplated can occur.

While these

events, described as "Black Swans", are not part of a model, a disciplined method

of risk management, used in conjunction with a CAT model, will minimize or eliminate shock losses that could affect a portfolio.

CAT Modeling Practice Operating the software is only a small part of what it takes to effectively utilize CAT modeling within a business. As with any model that attempts to simplify and represent real world phenomena it is vital that there is a strong understanding of the appropriate usage and limitations of the model.

42Slide43

passion. innovation. accountability

.

Components of CAT ModelsSlide44

A CAT model is built up of a number of modules that must all operate in coordination to produce the desired risk assessment. It is important to note that two of these (the hazard and vulnerability modules) could be considered individual models in their own right, and the combination of one feeding the other brings with it challenges that need to be understood.

The

vendors of

CAT models create and fix a set of events. While these are a small subset of the range of potential outcomes they provide a sensible number of scenarios that will represent the underlying hazard, while remaining at a practical level to make quick decisions. These

events are run consistently each time a model is operated, so there is no random element involved, and they can be compared between risks and between different companies using the same model.

As they only represent a subset, increased uncertainty should be applied to events at the extreme tail. Components of CAT Models

44Slide45

Components of CAT Models

MODULE 1:

Exposure data module Every CAT

model needs an input of risks against which an assessment is to be made. This usually consists of capturing multiple details about a risk, along with recording insurance policy terms. The two essential features of a risk that need to be known are the geo-location and an

insured value. After this, depending on the type of risk, there might be options to enter primary characteristics, such as construction type or year built for a property risk, and even secondary characteristics such as roof type. Some models provide approaches for geocoding locations based on addresses, and for estimating characteristics if unknown. MODULE 2: Hazard moduleEach generated event is tagged with the core components relevant to the hazard.

For

a

hurricane

this might be landfall location and direction of travel, peak wind-speeds and central pressure; for an earthquake this would normally be the

epicenter

and magnitude. The hazard module must combine this information with the exposure data being provided and any information the model has on salient features such as surface roughness (for windstorm hazard) or soil type (for earthquake hazard) at each location. For each event an assessment of the hazard impact at each location being assessed must be established.

45Slide46

Components of CAT Models

MODULE 3:

Vulnerability moduleThe resulting output of the hazard module is then passed to the vulnerability module. The hazard at any one location is independent of the risk that is actually there, but what we are interested in is

how the risk at that location will respond to the predicted hazard conditions. The vulnerability module contains a number of vulnerability curves, with the appropriate one chosen depending on the primary characteristics of the risk. These are often

derived from engineering studies or past experience, and represent how a risk will respond under different conditions. For an earthquake, for example, peak ground acceleration (PGA) is often the most important factor when considering how badly damaged a building will be. As the PGA increases, so does the expected damage. The relationship between the two is described in the vulnerability curve. Secondary characteristics, if provided, will often be used to make minor modifications to the vulnerability curves. The result of these calculations is a damage ratio to be applied to the risk at the given location. 46Slide47

Components of CAT Models

MODULE 4:

Financial module Armed with the expected damage ratios for each location that we are assessing, the CAT model can then begin to accumulate upwards through the

financial and insurance terms. Starting

with a calculation of the Ground Up loss to the individual location, the financial module will typically accumulate through location-level terms, to policy and then program level conditions, at each stage applying limits, deductibles, and special conditions that have been coded into the model. The resulting output is an Event Loss Table that provides an assessment of the financial risk exposure to individual events. This can then be combined to an exceedance probability (EP) curve to give further measures for the entire risk.

47Slide48

passion. innovation. accountability.

Data Quality

>Slide49

Ever since the first commercial catastrophe models became available (AIR Worldwide - 1987, Risk Management Solutions - 1988, EQECAT - 1994), there have been

questions about

their reliability

. But one thing is certain: the quality of data that goes into the model plays a pivotal role in the quality of results that are generated.

As CAT models and their results have become an established part of the insurance and reinsurance landscape, the industry has become

more reliant on their results.Modeled results contribute to rate making, aggregation potential, developing capital contributions and adding risk to the portfolio among other things.With each new release the modeling companies expand

their catalog of available

regions

and perils

update

methodologies

based on

lessons learned from past events and new scienceimprove functionality through the betterment of

the design and technology. Similarly, model users have made improvements in data capture and granularity.Data Quality is Key

49Slide50

Models can accommodate extensive additional refinements through secondary modifiers beyond the required primary attributes, or data. It is essential that the data be complete and accurate, as if it is not the model

will produce

inaccurate results,

potentially affecting pricing, capacity offered, and limits purchased.What if data is missing? The models can accommodate missing information to some extent; however, this increases the uncertainty

around the modeled results. The more uncertainty, the more compensation in terms of the

premium that insurers will likely need.Models also “keep score” and look at a number of factors based upon the primary and secondary attributes provided. A “bad” score in any one of the data categories can hurt an insured both in relation to pricing, capacity, and limits purchased, and in an insurer’s confidence that the insured understands its risk.

Let’s review an actual portfolio and how it scored after the first model run…

Data Quality is Key

50Slide51

Data Quality is Key

Exposure Stratification

In

the example provided, the

secondary modifiers

score is poor at 11%, and knowledge of locations

’

construction

types

could be improved from 89%.

Conversely

, the

geocoding and occupancy scores are quite high, demonstrating the insured’s understanding of these categories.

51Slide52

Data Quality is Key

Accurate

Primary and Secondary Attributes/Modifiers Are Critical

A broker should help clients accurately obtain their primary attributes/modifiers (including addresses, geocoding, construction, occupancy, number of stories, and year built) as well as their

secondary attributes/modifiers (including roof geometry, roof anchorage, maintenance programs, presence of parapets, equipment on roof, external ornamentation, and roof sheathing).We

strongly recommend insureds invest in CAT modeling or work with a broker who provides modeling as part of their compensation. Properly used, CAT models maximize clients’ buying power, allowing them to make informed decisions

proactively

design a

pre-emptive marketing strategy

differentiate

their risks for the negotiation of favorable

terms

create transparency around the sharing of assumptions with underwriters and internal decision makers, and implement risk-based allocations. Qualified modelers working alongside engineers ensure

such a process.52Slide53

Data Quality is Key

Challenges

to ensuring data quality

Data collection is both difficult and costly. Insurers write many policies and cover many

locations within their book of business. It takes a great number of man-hours and dollars to inspect new and renewal business.

Insurers‘ methods of storing data can also be problematic. Some legacy systems do not transfer schema standards to the database as well as others. But with most, if not all, insurers using one or more of the models in house, this should become less of an issue over time.

There is a

need for detailed and accurate

data collection

by insurers which captures:

Values

- proper insurance-to-value (ITV) is a significant factor in the model's ability to simulate a loss close to what actual loss would be;Limits

- specifically for commercial/industrial business, the more accurate the business interruption (BI) limits, the closer simulated results are going to be to an actual event;53Slide54

Data Quality is Key

Challenges

to ensuring data quality

Modeled results contribute to rate making, aggregation potential, developing capitalcontributions and adding risk to the

portfolio among other things.With each new release the modeling companies expand their catalog of available regions

and perils, update methodologies based on lessons learned from past events and new science, and improve functionality through the betterment of the design and technology. Similarly, model users have made improvements in data capture and granularity.

Data quality remains an industry-wide issue

and will

require continued

cooperation

from

all members

(insurers, reinsurers, brokers, and modeling companies) in order to continually improve exposure information. Such efforts should ensure the industry remains robust and able to withstand future catastrophic events

, while providing essential cover for those exposed to windstorms, earthquakes and other natural perils.54Slide55

Data Quality is Key

Resolution/geocoding

– the ability to model street address as opposed to a lower

level resolution (e.g. zip code) can have a dramatic impact on the modeled loss, specifically in coastal regions that are affected by wind events

;Primary characteristics – construction and occupancy information; andSecondary

characteristics

– differing

by exposed

peril (e.g. roof type, year built

, square

footage etc. for hurricane models and soil type, number of stories etc. for earthquake models

). Once the data has been collected, care needs to be taken when creating the database so as to ensure the information is interpreted correctly by the model.

It is easy to notice information that is missing, but more difficult to identify where something has been entered or coded incorrectly, especially when looking at large datasets.

55Slide56

Data Quality is Key

The

implications of data

quality (insurers)CAT modeling results are largely ineffective without quality data collection. For insurers

, the key risk is that poor data quality could lead to a misunderstanding regarding what their exposure is

to potential catastrophic events. This in turn will have an impact on portfolio management, possibly leading to unwanted exposure distribution and unexpected losses, which will affect both insurers' and their reinsurers' balance sheets.CAT modeling results are also used by insurers to

anticipate the financial effect a

catastrophic event

may have on its portfolio/balance

sheet and

to assist with the purchasing of

reinsurance limits. If results are skewed as a result of poor data quality, this can lead to

incorrect assumptions, inadequate capitalization and the failure to purchase sufficient reinsurance protection.Buildings

come in many shapes and sizes, old and new. All of these buildings are very different, but can look the same to a CAT model if the proper defining data elements are not maintained in a dataset.

56Slide57

Data Quality is Key

The implications of data quality

(reinsurers)

While data collection is the responsibility of the insurer, reinsurers place a high level of importance on quality of the exposure data

that is provided as it has an effect on their underwriting decisions and portfolio profitability. The higher quality of data an insurer

can provide, the greater the credibility a reinsurer will give for a modeled result.Insurers can tap many sources of information (modeling companies, CAT management consultants, reinsurers), to improve data quality within their portfolio. An insured's ability to provide a high level of data quality as part of their reinsurance submission,

would only

enhance their

reputation

within

the reinsurance

market place.Companies like Marshall Swift Boeckh (MSB), ISO and AIR Worldwide have

developed products to aid re/insurance companies in determining the value of a structure. The systems utilize databases with

structured algorithms and capture the building characteristics in calculating the value (residential and commercial). While these systems are not infallible, they provide a structured and consistent approach in the assessment of value.

57Slide58

Data Quality is Key

The implications of data quality

(the industry as a whole)

Standardized data is also an important step towards improving data quality for the industry as

a whole. 58

The ability to use

standardized

data across

different platforms

will improve

the accuracy

and simplify the compiling of data. As a result, many re/insurers have adopted or

are planning to adopt ACORD (XML) standards.Facilitating this development of standardized data was the main impetus behind the formation of ACORD, a nonprofit standards development organization serving the insurance industry

and related financial services industries.Slide59

Data Quality is Key

Understanding uncertainty

For reinsurers, a

multi model approach can only improve the analysis as all modeling

companies have differing views of catastrophic events. While the analysis results from various models tend to converge for industry-wide portfolios

, differences can be significant on a more granular level. A company's comprehensive understanding of the strengths and weaknesses of the models will allow them to appropriately weigh the results of the model that works best for a specific peril and region.When a specific data attribute is not available

, it

is often coded as unknown

. Examples include:

the

year a structure was built,

the number

of stories of a building, the basic construction type and how the building is being occupied.

These four primary building attributes were once elusive and often not completed or set to a default value. Now, many organizations can accurately extract this information from their core processing system, making it part of the information value chain.Nevertheless, when one of these data attributes is not known, a model will utilize

an "average" value based on research results for that particular region. The year-built attribute is a field that has become far more

important in determining potential loss. The year a structure was built in the state of Florida, for example, has a significant impact on its ability to withstand a hurricane.

59Slide60

Data Quality is Key

However, when

the year-built

is unknown, a catastrophe model will use an average value, which then increases

the uncertainty of the result. The difference in the expected loss against the "real" value can be significant

(plus or minus), and the uncertainty around that figure can be a factor greater than the known value.Drilling past the primary characteristics, CAT models also reflect secondary building characteristics to help companies

differentiate between

the finer features that a risk

may have

. These include the shape of the roof

, architectural

elements, parapets and overhangs, and many other fields too numerous to

mention. Improving the modelsWhile insurer and reinsurer data collection is an essential ingredient in improving the accuracy of modeled results, it is not the only ingredient. Improvements made to the catastrophe models themselves, either through advances in computer power, new scientific knowledge or lessons learned from actual events, will also help elevate accuracy

. 60Slide61

Improved Models

Modeling

companies can influence the

industry by setting data standards and guidelines that are

important in modeling. Converting from building's fire classification to an actual building type (i.e. non-combustible versus reinforced

concrete), is one example of how the quality of data has matured over the last ten years.There are many lessons to be learned from each catastrophic event that occurs and these opportunities

are well utilized by the

modeling companies

. After every event, teams

of scientist

and engineers survey the

damaged regions to study how structures perform. Post event claims analysis is conducted

and combined with the on-site survey results to refine the model's vulnerability functions. Every event is viewed as an opportunity to calibrate the models and improve their ability

to simulate perils with greater accuracy.The modeling companies also work extensively with insurers to increase the understanding of the model capabilities. This includes emphasizing the benefits to be gained

from committing time and effort to collecting quality data. The modelers continue to push for detailed (street address if possible)

data collection as opposed to aggregated data, which may use the centroid of a region and add significant uncertainty to the hazard assumption. The collection of detailed exposure data provides

insurers with a better knowledge of their portfolio and its risk. This knowledge can be passed along to reinsurers who are then

able to use it with other submission details to develop a comfort level and better understanding of the insurer and its business.

61Slide62

passion. innovation. accountability.

Secondary Modifiers

>Slide63

Secondary Modifiers

What are secondary

modifiers (aka characteristics)?

There are certain data points required to model a given risk; secondary modifiers are additional data points that provide more detailed information on structural integrity and building characteristics, including construction quality, roofing details, cladding, opening protections such as storm shutters, and so on. The list is comprehensive and changes occasionally with the upgrade of modeling versions, so it is important to

periodically review the Statement of Values (SOV). Models

can then make the proper interpretations.Why are secondary modifiers used?Proper assessment and inclusion of modifiers can have a significant impact on the modeling results. Accurate secondary modifiers can help the underwriter, broker and insured better understand the exposure inherent in a SOV. This knowledge of expected losses helps insureds and insurers set acceptable program structure and sublimits.

63Slide64

Secondary Modifiers

CAT

Modeling: The Benefit of

Including Secondary ModifiersIn recent years, catastrophe (CAT) modeling for hurricanes and earthquakes has become an essential

resource for all players in property insurance, in particular:

Underwriters use CAT models to accurately assess risk and determine capacity and pricing; Brokers look to the models to help in program design; and

Insureds

use modeled results

to better

understand exposures in their statement of values (SOV).

As reliance on

CAT modeling grows, so does the need to better understand the numerous features that impact results, including secondary modifiers.64Slide65

How does the market use secondary modifiers?Markets prefer information that is as detailed and accurate as possible for their own analysis because it bolsters confidence

in their underwriting decisions

.

Full disclosure of information, both good and bad, helps protect against unforeseen losses and underwriters are more apt to bind the coverage if they are comfortable with

the information provided. Data accuracy also bolsters the relationship with the market, as an aim for accuracy and not the lowest price, builds trust.

The benefit of including secondary modifiers is that underwriters and actuaries should feel more comfortable with the model output when the quality of the input data is better. Better data leads to better decisions.Even if the available information is unfavorable, it helps identify the risk – for both the insurer and the insured

– so

that there are no surprises, unanticipated gaps or skewed expectations in the unfortunate event of

a catastrophe

. Regardless of the impact of modifiers, insureds should declare and include this information to avoid allegations of misrepresentation

.

Secondary Modifiers

65Slide66

Secondary Modifiers

How do secondary modifiers benefit insureds?

It also helps underwriters

customize coverage to suit a specific risk involved.CAT models are instrumental in showing insureds where their greatest exposures lie. One product of

CAT modeling is a heat map, or an analysis that isolates SOV locations that are heavy drivers of average annual loss (AAL) and probable maximum loss (PML). Heat maps can help

identify specific locations where secondary modifiers could impact or influence coverage within a portfolio of properties. Further analysis can determine construction upgrades that will strengthen buildings against catastrophes. Including secondary modifiers also makes an account more attractive to underwriters; if secondary modifiers are left out of the equation, underwriters will make decisions using an assumption of “average” for model inputs. Depending

on the peril being modeled, the

potential PML differential when secondary modifiers are present

can swing

results by 50 percent or more

, in either

direction. Information used in modeling, including secondary

modifiers, should come from a qualified third-party group to help ensure its veracity.66Slide67

Secondary Modifiers

Conclusion

Beyond

potentially affecting pricing and capacity offered, it is worth noting that certain modifiers are rather easy to get and can provide more accurate modeled

results. For example, if a structural engineer develops a certified plan that details design review (including building characteristics such as the age and shape of the roof), it can provide a better picture of what will happen when the

structure is exposed to certain stresses or forces. Further, best case scenarios can be used for a cost / benefit analysis of upgrades, such as using adding hurricane shutters to a particular location.Gathering complete information and data points are essential for predictive modeling. Detailed information about a building’s construction, coupled with its usage, provide a predictive image of how the building will react in the event of a catastrophe.

And

with better data comes

more informed decisions

for insureds

and markets

.

67Slide68

Secondary Modifiers

The Location Import Template captures both primary and secondary characteristics

68Slide69

Secondary Modifiers

Earthquake Secondary Modifiers Impact Guide

The Earthquake Secondary Modifiers Impact Guide shows how various secondary modifiers affect the loss estimates of certain construction classes.

Depending on the construction class of a given building, the impact can range from

an

increase in loss estimate by >20%; 5-20%; or <5%, no change in loss estimate to

a

decrease

in loss estimate by

<5%; 5-20%; or >20%

In some cases, the modifier is not relevant and no change is contemplated in the model.

69Slide70

passion. innovation. accountability.

A Closer Look at

20 Secondary EQ Modifiers

>Slide71

1. Base Isolation

2. Cladding Type

3. Construction Quality

4. Engineered Foundation

Affects how much energy of the EQ enters the structure.

Little or no structural value; but damage can be severe.

Considers workmanship and quality of construction materials.

Yes / No

Glass / Precast Concrete / Unreinforced Masonry

Good / Average / Poor

No / Yes

Foundations that are explicitly designed to withstand soil deformations anticipated for landslides or liquefaction cause the building to perform better.

“Yes” will affect the model results based on the degree of landslides or liquefaction hazard present at the location modeled.

71Slide72

5. Cripple Walls

No / Braced / Unbraced CW

Could lead to total loss of building.

6. Equipment

Support Maintenance

7. Frame

Foundation Connection

Good / Average / Poor

Bolted / Unbolted

Buildings showing fatigue, distress, cracking etc. will likely sustain above average damage.

Lack of “positive” connection between structure and its foundation can cause a building to slide of its foundation.

72Slide73

8. Mechanical andElectrical Equipment EQ Bracing9. Ornamentation

10. Plan Irregularity

Is equipment properly anchored to floor or roof and/or against structural elements?

Decorative elements (parapet walls, cornices etc.) can fall off during an EQ.

Well braced / Somewhat braced / Unbraced

Little or none / Average / Extensive

Regular / Irregular

Irregular shaped buildings tend to twist in addition to shaking laterally.

11. Pounding

No / Yes

Occurs when there is little or no clearance between adjacent buildings.

73Slide74

12. Purlin Anchoring13. Soft Story

14. Short Column

Addresses the connections between tilt-up walls and the roof framing system to resist the load due to EQ shaking.

Addresses buildings that have shear walls or infill walls at upper floors that are interrupted at the first floor to provide open space for parking.

Properly anchored /

Not properly anchored

No / Yes

No / Yes

When columns in a reinforced concrete moment frame used to resist seismic loads are effectively shortened in height by the presence of spandrel beams or infill walls used as architectural elements. Increases shear forces.

74Slide75

15. Sprinkler LeakageSusceptibility16. Sprinkler Type

17. Structural Upgrade

(non URMs)

How susceptible is contents, interior partitions and fixtures to water damage?

Low / High

Wet / Dry

No / Yes

Applies to a building that has been retrofitted to provide superior EQ performance relative to other buildings of similar construction, occupancy, height, and vintage. Used if the upgrade conforms to a more stringent building code that that in use when the building was originally designed and constructed.

18. Unreinforced Masonry

Partitions or Chimneys

No / Yes

Unreinforced masonry is extremely vulnerable to EQ-induced ground motions.

The model automatically assumes that a building’s sprinkler system is 70% wet pipe and 30 % dry pipe. This modifier is used where the system is known to be wet or dry.

75Slide76

19. Unreinforced Masonry RetrofitUnreinforced masonry is extremely vulnerable to EQ ground shaking. This modifier applies to the performance of load-bearing unreinforced masonry walls only. See also modifiers “Cladding Type” and “Unreinforced Masonry Partitions and Chimneys”

No / Yes

20. Vertical Irregularity

Significant setbacks and overhangs can create stress concentrations that will experience above-average levels of damage during an EQ.

Regular (No) / Irregular (Yes)

76Slide77

Case Study: Sample Portfolio77Slide78

RMS 13.0 Results based on Primary Characteristics Only 78

EQ / EQSL

250 year and 500 year return period PMLs: $69.9M and $98.7M, respectively.

AAL: $1,491,209

Windstorm /

Storm Surge250 year and 500 year return period PMLs:

$26.7M

and

$38.5M

, respectively.

AAL:

$622,834Slide79

The Impact of Secondary CharacteristicsFor the purpose of this presentation, we applied the following secondary characteristics to the original Statement of Values:

YEAR

UPGRADED

Any building more than 20 years old, a “YEAR UPGRADED” was added to reflect “normal” time frames used to upgrade both Commercial and Residential structures (dates varied from 2002-2010).

EARTHQUAKEPlan Irregularity: all CA locations were modified from “UNKNOWN” to Option 1 “Regular”

Soft Story: all CA locations were modified from “UNKNOWN” to Option 1 “NO”.Vertical Irregularity: all CA locations were modified from “UKNOWN” to Option 1 “NO”.Short Column: all CA locations were modified from “UNKNOWN” to Option 1 “NO”.Ornamentation: all CA locations were modified from “UKNOWN” to Option 1 “Little or None”.Cripple Walls: all CA locations were modified from “UNKNOWN” to Option 1 “No Cripple Walls”Construction Quality

: all CA locations were modified from “UNKNOWN” to Option 1 “GOOD”.

Pounding

: all CA locations were modified from “UNKNOWN” to Option 1 “NO”.

Engineered Foundation

: all CA locations built from 1985 and newer were modified from “UNKNOWN” to Option 1 “YES”.

WIND/STORM SURGE

Roof Covering: modified from “UNKNOWN” to Option 4 “ Built Up roof or Single Ply Membrane Roof with the presence of gutters for Commercial Buildings and Option 7 “Normal Shingle (55mph) for Residential Buildings.Roof Age/Condition: modified from “UNKNOWN” to a mixed blend of 6-10 years/11+ years for commercial buildings and a blend of 0-5/6-10 years for Residential Buildings depending on the age of the original roof.

Roof Geometry: modified from “UNKNOWN” to Option 1 “Flat roof with Parapets” for all Commercial Buildings and Option 5 “Gable Roof (slope < 26.5 degrees)Cladding Type: modified from “UNKNOWN” to Option 1”Brick Veneer” for residential locations built prior to 1990 and Option 4 “EIFS/Stucco” for locations built 1990 and newer.79Slide80

RMS 13.0 Results with Secondary Modifiers Applied80

EQ / EQSL

250 year and 500 year return period PMLs: $44.2M

(-37%)

and $63.5M

(-36%), respectively. AAL: $886,660 (-41%)Windstorm / Storm Surge

250

year and 500 year return period PMLs:

$16.2M

(-39%)

and $23.8M

(-38%) , respectively.

AAL: $301,006 (-52%)Slide81

RMS 13.0 Results with Secondary Modifiers Applied81Slide82

RMS 13.0 Results with Secondary Modifiers Applied82

When we run the model by insurance layer, we gain valuable insights into Loss Expectancies and AAL’s for different tranches of the insurance

program.

[We did not run all the layers, this is why the ground up totals are slightly larger than the sum of all totals for layers up to $100M]

AAL

250-year

500-year

Primary $10m

$436,941

$10,000,000

$10,262,347

$15m xs $10m

$221,331 

$15,000,000

$15,000,000

$25m xs $25m 

$135,124

 $18,433,148

$24,998,769$50m xs $50m $73,613

$0$12,264,092

  $867,009 $43,433,148

$62,525,208

Delta to "Ground Up"

-$19,651

-$819,277

-$978,253Slide83

RMS 13.0 Results with Secondary Modifiers Applied83

AAL

calculations can

also be

used to:

Identify where a closer look at the data provided may be warrantedCompare the relative severity of exposure at different locations

Establish priorities for loss mitigation efforts

Assist in the allocation of premium

Improve disaster recovery

plansSlide84

Case Study: 101 year old Smith Tower (Seattle, WA)

84

Smith Tower

is a skyscraper in Pioneer Square in Seattle, Washington.

Completed in 1914

, the 38-story, 149 m (489 ft) tower is the oldest skyscraper in the city and was the tallest office building west of the Mississippi River until the Kansas City Power & Light Building was built in 1931. It remained the tallest building on the West Coast until the Space Needle overtook it in 1962. Smith Tower is named after its builder, firearm and typewriter magnate Lyman Cornelius Smith, and is a designated Seattle landmark.

Factors:

Ornamentation

Pounding

Plan Irregularity

Vertical Irregularity

Structural Update

Age

Mechanical BracingBase Isolation?Historical Building ValuationSlide85

passion. innovation. accountability.

What’s Next?

>Slide86

What’s Next?

Frustration with Property CAT models is leading to Change

Traditional models

too

focused on aggregation of risk that insurers tend to calculate, rather than individual exposures and properties.tend to change from year to year in ways that do not reflect actual changes in loss exposures.

were never designed with risk managers in mind but with insurers in mind.Risk Managers are looking for a clearer picture when it comes to CAT loss modeling, an area fraught with confusion and increasing criticism.

want more control

over how

their specific exposures

generate loss estimates and how those estimates are calculated.

w

ant to be able to drill down

into second and third tier modifiers.want to eliminate the “blind spot” on how models project losses.

There is now a move towards “Open” systems and “transparency”. 86Slide87

What’s Next?

Frustration with Property CAT models is leading to Change

“Open Source” Models – jointly developed by scientists, engineers and industry sectors

Insurance Journal:

The

Oasis Loss Modeling Framework has unveiled what it describes as “the most significant development in the modelling of natural catastrophe losses for 20 years”— the launch of an independent, global, open framework for use by any party with an interest in creating a catastrophe model.It’s owned by its members and is not-for-profit. It is designed to “bring down the cost of modeling, as well as providing transparency and greater flexibility for users.”

Membership

fee is £20,000

. As other revenue sources come on stream, this figure is expected to reduce substantially.

Members

get direct access to the code and participation in the

community

working

parties.87Slide88

What’s Next?

Oasis Loss Modeling Framework

London-based nonprofit representing 25 insurers, reinsurers and brokers

strives to offer lower cost, transparency, greater flexibility via program that is open to anyone with interest in creating new CAT risk model

offers access to the best breed models tailored for specific hazards and regions (

e.g. ImageCat with focus on EQ; Spa Risk LLC; RiskInsight, JBA Risk Management)single portal; risk manager can download software for free, search for a model of the geographical region and peril in question and negotiate a fee with the provider

allows user to look at many different

models’ views

88Slide89

What’s Next?

“Open” systems – allowing user interface – and “transparency”

Touchstone

[AIR Worldwide]

open platform, allowing to import 3rd party hazard layers or run multiple alternative models on a single platform for a more complete view of risk models

use can overwrite some of AIR’s assumptions to better reflect their experienceFirms providing data and models through Touchstone include Ambiental, ERN, EuroTempest, HIS Inc., KatRisk, Met Office, PERILS, and SSBNRisk Quantification & Engineering (RQE) [CoreLogic EQECAT]inherently open platform; additional models or components (e.g. hazards, vulnerabilities) can be added

High granularity of reports down to individual site levels; very extensive documentation; analyses of drivers of risk

Aides in dealing with regulators (Solvency II in Europe,

O

RSA in the US)

RMS(One)

[RMS]system of record for all of the risk items in the businesscan run RMS and other models; helps understand the impact of different scenariose

xposure and model agnostic89Slide90

What’s Next?

RMS

– North Atlantic Hurricane Models Version 15.0

90

How will it affect model results?

In summary, the Aggregate Exceedance Probability (AEP) and Average Annual Loss (AAL) Loss Changes for Wind and Surge from V13.0 to V15.0 will change as follows:

All US (including TX, Gulf, Florida and Hawaii):

Will

reduce by 0% to 10%

Southeast

, Mid-Atlantic & Northeast:Will

increase by 0% to 10% The pressure on underwriters to further reduce or hold existing Wind/Surge rates is obvious based on location of risk.Release: March 31, 2015Slide91

passion. innovation. accountability.

CAT Modeling Terminology

>

Appendix 1Slide92

CAT Modeling Terminology

The CAT modeling

industry is full of terminology and acronyms, many of which have been borrowed from mathematics or actuarial

modeling. What follows is an explanation of some of the most common ones used by CAT modelers

. EP Curve An EP curve communicates the

probability of any given financial loss being exceeded. It can be used in one of two ways: provided with a financial loss the EP curve could be read to give you the probability of this loss (or a greater loss) occurring; or alternatively provided with a probability level the EP curve could be read to show you the financial loss level that this corresponds to. It is important to note that this refers to a loss being exceeded, and not the exact loss itself. This approach is used for CAT modeling, as it is beneficial to identify attachment or exhaustion probabilities, calculate expected losses within a given range, or to provide benchmarks for comparisons between risks or over time. Calculating the probability of an exact financial loss is of little value.

92Slide93

OEP and an AEP curve OEP stands for Occurrence Exceedance Probability; AEP

stands for

Aggregate Exceedance Probability

. The OEP represents the probability of seeing any single event within a defined period (typically one year) with a particular loss size or greater; the AEP represents the probability of seeing total annual losses of a particular amount or greater.

They can be used in tandem to assist with managing exposure both to single large events, as well as accumulations of multiple events across a period.

CAT Modeling Terminology93Slide94

VaR and TVaR (1 of 2)VaR stands for Value at Risk;

TVaR

stands for

Tail Value at Risk. They are both mathematical measures used in cat modeling to represent a risk profile, or range of potential outcomes, in a single value. Value at Risk

is equivalent to Return Period, and measures a single point of a range of potential outcomes corresponding to a given confidence or fixed position. When used to compare two risks, in conjunction with the mean loss, it communicates a measure of uncertainty in the loss assessment. Tail

Value at Risk (or Tail Conditional Expectation) measures the mean loss of all potential outcomes with losses greater than a fixed point. It helps to communicate ‘how bad things could get’. When used to compare two risks, along with mean loss and Value at Risk, it helps communicate how quickly potential losses tail off. CAT Modeling Terminology

94Slide95

VaR and TVaR (2 of 2)With current modeling techniques any EP curve is limited by the number of theoretical events or simulation years used to make it up. In the tail of a distribution there can be large jumps between individual points.

Value

at Risk points read at high return period / confidence levels can perform strangely as the limited number of sample points makes figures jump back and forth between assessments.

The TVaR measure provides a small amount of protection against this effect. By considering the average of all points in the tail it is less sensitive to such effects and can provide a more stable measure.

However the TVaR is necessarily reliant on the quality of modeling in the tail of the distribution, where models will always be fairly weak.

CAT Modeling Terminology95Slide96

Event Loss Table (ELT) An ELT is a collection of theoretical cats (hurricanes, earthquakes etc.) along with the modeled losses estimated to occur from each event. This forms the raw data that is used to build up EP Curves and calculate other measures of risk.

Coefficient

of Variation (CoV)

The CoV is the standard deviation divided by the mean (annual average loss). The wider the variation on the distribution of data, the higher the

CoV.

CAT Modeling Terminology96Slide97

Difference between Near Term, Long Term and Historical rates Models for North Atlantic Hurricane need to take into account the strong influence that global climate and oceanic conditions have on them, potentially affecting everything from frequency and strength to landfall location. Long

term or Historical analyses

use all available information on past

hurricane activity (stretching back to around 1850) to advise on likely frequencies to be seen in the coming year. Near Term analyses by AIR (which are referred to as Medium Term analyses by RMS) attempt to better represent current conditions.

AIR does this by marking each historic year as either having the Atlantic in a “warm phase” (where sea surface temperatures in the Atlantic are warmer than the long term average) or a “cold phase”. At present we are assessed to be in a “warm phase”, so AIR uses only historic years in a similar phase to advise on likely frequencies for the model

. RMS takes a different approach, instead eliciting a number of academic “models” designed to forecast the next 5 years of events. They then apply a weight to each model according to how accurately it is able to represent the previous 5 years, to form a blended assessment of future frequencies.

CAT Modeling Terminology

97Slide98

Difference between Ground Up, Gross, Net and Final Net losses Ground-up loss

is

the loss to the policyholder or risk insured;

Gross loss typically refers to claim made to insurer;

Net loss typically refers to gross loss net of reinsurance;

Final net loss typically refers to the gross loss net of reinsurance and reinstatements.

CAT Modeling Terminology

98Slide99

passion. innovation. accountability.

Secondary Modifier Tables

>

Appendix 2Slide100

RMS will “default” to the “worst case” characteristic when a field is left BLANK.

EQ Secondary Characteristics

100Slide101

RMS will “default” to the “worst case” characteristic when a field is left BLANK.

EQ Secondary Characteristics

101Slide102

RMS will “default” to the “worst case” characteristic when a field is left BLANK.

Wind/SS Secondary Characteristics

102Slide103

RMS will “default” to the “worst case” characteristic when a field is left BLANK.

Wind/SS Secondary Characteristics

103Slide104

passion. innovation. accountability.

Wind/Surge Secondary

Modifier: Roof Anchors

>

Appendix 3Slide105

Wind/Surge – Secondary Modifier: Roof Anchors

Toe Nailing - No Anchoring / Clips / Single Wrap / Double Wraps / Structural

105

Roof anchors are used to connect the roof framing elements (i.e. rafters, trusses, or joists) to the supporting walls.

Buildings

that do not have properly-sized and installed connections between the roof and supporting walls are susceptible to severe

damage

when the entire roof system is lifted off the building by a windstorm.Slide106

passion. innovation. accountability

.

Frequently Asked Questions

Appendix 4Slide107

FREQUENTLY ASKED QUESTIONS (Source: Lloyd’s Market Association)1. Why is it that every time an event occurs I hear that it was not covered properly by the

CAT

models?

A model is only a representation of reality. Depending on the questions being asked a model could be highly complex or extremely simple, and it is in understanding the limits of a model that its value can be properly achieved

. First and foremost it must be understood what a model is attempting to represent in the first place. More recently model vendors have begun to explicitly state the elements of loss that their model is intended to represent, and more importantly they have started to identify known elements of loss that they explicitly do not cover.

CAT models do not pretend to cover all elements of all CAT risks worldwide, and it is therefore the responsibility of individuals to ensure that they clearly understand both of these. Vendors do continue to work to add to the suite of risks covered by their models, but this is a continual work in progress and is driven largely by market demands. However, even within risks that are covered we would still expect to see elements that are not perfectly represented. Producing a model of a real world phenomenon is only as good as the information that is available and the investment spent in studying it.

107Slide108

1. Why is it that every time an event occurs I hear that it was not covered properly by the CAT models? (continued)Loss Amplification (price increases following a major event caused by a scarcity of resources and an increased demand) is a known impact, but relatively little recorded information about it is available historically worldwide, and how it varies between events that occur once every 10 years to events that occur once every 100 years is almost non-existent.

An attempt to allow for this is included in a number of models, but it is highly likely that this will need to develop over time.

Models

must be considered in the context of the purpose for which they were designed. For most CAT models this is to assess the overall risk profile of a set of locations to particular hazards. To achieve this practically, certain assumptions and approximations are required. When used for its intended purpose these reductions should produce negligible impact, however

drilling down too far into any model will reach a point below which the model is no longer appropriate. The climate simulation models, used by the IPCC (Inter-governmental Panel on Climate Change) to estimate the impact of climate change on the planet, would do an appalling job of telling you what the weather will be like at your house on your birthday, but still remain valid approaches for predicting worldwide temperature changes over decades.

108Slide109

Why is it that every time an event occurs I hear that it was not covered properly by the CAT models? (continued)When an individual event occurs and the resulting profile is compared against the CAT models it is important to identify when an outcome casts doubt on a key assumption relevant to the overall value of the CAT model, or whether the particular features of the event simply fall outside of the subset of generated events, but within the consideration of the overall model.

109Slide110

2. What

is the impact of poor quality data on results?

A

model is only as good as the data that feeds it. Even if we had

perfect exposure data the challenge of CAT modeling is still huge, and the results that are produced will contain numerous

uncertainties. However if the input data is of poor quality then no amount of modeling will produce correct

output

.

Poor

quality data can be of two forms, inaccurate or

incomplete

. Inaccurate Data

Models are unable to identify inaccurate data, so will continue to assess the risk based on the information being correct. This means that output will be presented back to the user with no indication that the results being analyzed are inappropriate, and if this information continues to feed further down the chain incorrect decision making will follow. An incorrect location could put the risk further into a hazard zone, or further away. Incorrect primary characteristics could imply the location was more or less vulnerable than reality. If inaccuracies are minor and random and spread through a large enough portfolio of risks then are unlikely to cause too many problems, however if the inaccuracies are systematic, or if they occur on peak risks they have the potential to significantly mislead.

110Slide111

2. What is the impact of poor quality data on results? (continued)Incomplete DataIncomplete data causes problems for a different reason. Models need certainty to proceed, so missing information is usually replaced by estimates. This is beneficial in that it allows us to proceed with a

modeled

analysis even with information missing, but what is not always clearly communicated is the

additional uncertainty that this brings. In CAT modeling communicating and understanding the uncertainty is vital, however in the case of incomplete information no additional uncertainty is added to the results. If there were sufficient time to reprocess the analysis with the complete range of potential inputs it would be more obvious that the missing information will have introduced a far wider range of potential outcomes than is otherwise suggested. When dealing with natural

CATs the difference between building codes, or distances from the coast or a fault line can make the difference between a risk having no loss or being a total loss.

111Slide112

3. Why do I need aggregates if I have a model? CAT modeling is just one of many tools

in an arsenal for understanding and managing

CAT

risks. As noted there are many elements of CAT risk that cannot currently be modeled, or are in the early stages of being developed into a CAT model. Additionally, while

modeling helps to push the boundaries of loss forecasting, the limitations and uncertainties are unlikely to go away any time soon, and one must never lose sight of common sense approaches to managing risk.

The recording and monitoring of aggregate positions can provide a useful fall-back and sense check against which the complex output of CAT models can be reviewed and challenged. 112Slide113

4. What is a 1 in 250 return period? Future losses from CAT events cannot be accurately predicted. Instead the purpose of any form of

modeling

is to use what knowledge we do have about the likelihood of events occurring, along with estimates of the potential impacts that each event could have, to build up a picture of the range of potential outcomes.

To translate this range of outcomes into something meaningful it is common practice to select a fixed confidence level to report against. Asking for the 1 in 250 return period is, like gambling odds, simply an easier way to represent asking for the monetary loss in the range of outcomes

where only 1/250 = 0.4% of potential outcomes are worse. In mathematical terms this is the 1 – 0.4% = 99.6% confidence point, and you are stating that you are ‘99.6% confident’ that losses will not be larger than this value.

‘Return Period’ figures must therefore always be considered within the context of the analysis. For example: Which regions and perils have been included in the assessment, and are there additional potential losses not included? It is important to note that this is simply a way of representing how confident you are about potential loss outcomes being reviewed and is not directly intended to be translated into a multi-year assessment of event frequency, where other considerations would be required. 113Slide114

5. Why do 1 in 100 year losses happen every few years? ‘1 in 100’ relates to the probability of a loss in a particular region to a particular peril.

Imagine

you have a 100 sided dice. With just one dice your chance of rolling a 100 would be 1 in 100 or 1%. However, if you had 10 dice, your chance of rolling a 100 would be 10 times greater - so 10%.

The different dice represent the different perils and regions that are insured around the world so, unfortunately, ‘1 in 100 year events’ should be expected every 10 years, if not more frequently.

114Slide115

6. What is an n-year event? The “events” used by the models are theoretical and used as vehicles to support the calculations, and should be used with caution. EP curves are then built up considering all events and scenarios, and how they interact with each other and the final resulting curve should be considered as separate from the individual events that went to make it up. This EP curve can now be used to reach your n-year loss, but there is no such thing as an n-year event.

The

trouble with converting the purely financial EP curve to a real world comment on events can be seen when you consider that a rare Category 5 hurricane that skims the coast can cause far less financial loss than a more frequent Category 3 that drives onshore.

Additional useful information can be gained from looking at the events or scenarios that cause losses at the n-year level; however

it is important to remember that there are a large number of different combinations that could achieve the same result and it will not always be possible to determine this from the model alone. For example, careful examination of the EP curve may lead you to find that your n-year loss is being strongly driven by one country/peril or another; or that you have more or less exposure to single large events than multiple small events.

115Slide116

7. Why can you not add up return period losses? A ‘Return Period Loss’ is the monetary amount, given a range of potential outcomes, where a given fixed percentage of outcomes result in worse monetary losses (see also ‘What is a 1 in 250 return period?’). Combining two analyses means combining two sets of potential outcomes. In some cases the two sets may be independent, leaving you simply with a single larger set of outcomes. In other cases the two may interact – perhaps a large loss outcome from the first analysis is linked to a large loss outcome in the second, such as if both have been caused by the same theoretical Hurricane.

The

new ‘Return Period Loss’ for the combined analysis now depends heavily on how the two different sets of outcomes interacted, which can’t be seen by looking at the individual analyses alone, and must be recalculated once the grouping has been performed.

116Slide117

7. Why can you not add up return period losses? (continued)

Example

to illustrate combining two event sets

117Slide118

8. What is Pure Premium? The Pure Premium represents the average of all potential outcomes considered in the analysis, and could be considered to be the

break-even point if such a policy was to be written a very large number of times.

The nature of

CAT risk means that the profit made when actual losses are lower than this assessed average is heavily outweighed by how much of a loss you could have when actual losses are higher than this assessed average; and that real experience will be very ‘spikey’ i.e. several years of no loss, followed by a large loss. Because of this underwriters usually add an

“uncertainty” load to reach a technical premium which the models can assist with calculating. In addition, the actual premium charged by underwriters should include consideration for potential losses not included in the

modeled assessment, these can include claims handling capabilities, moral hazard, loss record, Loss Adjustment Expenses and other perils (fire, flood, theft etc). 118Slide119

9. How can I still get a loss to a layer when the mean loss is less than the attachment point? Future

losses from

CAT

events cannot be accurately predicted. Instead the purpose of any form of modeling is to use what knowledge we do have about the likelihood of events occurring, along with estimates of the potential impacts that each event could have, to build up a picture of the range of potential outcomes. The mean loss given by a model is then just the average of this range of outcomes – the break-even point if this scenario were to be repeated a large number of times – however when applying financial structures the models retain the full range of potential outcomes, and use these when considering losses to insurance policies.

While the average loss may be below the attachment point, the uncertainty involved in predicting exact losses may mean that there are some potential scenarios that do in fact exceed the attachment. It is therefore important that we consider these when calculating possible losses to the written policy.

119Slide120

9. How can I still get a loss to a layer when the mean loss is less than the attachment point? (continued)

The above diagram

shows how losses can enter a layer despite the mean loss being less than the attachment point. Red bar = layer; Blue dash line = Ground up Average Annual Loss (GUAAL); Solid blue line = range of potential losses

.

To give an example, a model might suggest that out of 10 potential future years they will see 9 clean years, and have one year with a single $100m loss.

If you were to consider writing a $20m xs $20m policy on this risk the mean loss is $100m/10 = $10m, which is below the attachment point; however in reality you would have a 9-in-10 chance of a zero loss, but a 1-in-10 chance of a total loss of $20m, giving you an average loss to the policy of $2m.

120Slide121

10. Why is the 10,000 year loss in RMS not the worst case loss for this account or portfolio? This question confuses the AIR / simulation approach to

modeling

, with RMS’s approach.

AIR uses a simulation methodology prior to setting up their model, whereby they run their model to create a potential year of CATs 10,000 times. Each time the model is run you get different combinations of events, selected according to pre-coded frequencies. When we run the model in-house we get the resulting losses from these 10,000 potential simulated years. The EP curve that AIR builds up is created by ranking losses in descending order, and assigning each simulated year an equal likelihood of occurring. In this case the 1 in 10,000 year loss is the largest in the set.

RMS

takes an entirely different approach. Each event in their model represents a scenario with a range of uncertainty, and each scenario is given an “event rate” that represents a likelihood of occurrence (a weighting). EP curves are built up mathematically from all events in the catalogue, resulting in a final distribution of potential loss outcomes that stretches out as far as they are willing to calculate. In practice this will result in the model being able to give figures for return periods in excess of 1 in 1 million, although very little confidence should be given to the modeling anywhere near this part of the curve.

The

reality is that neither model can tell you what the “worst case” loss for the account actually is, because our knowledge of

CAT

is still developing. The only sensible answer to this is “total loss”. Both models are simply stopping calculations at an extreme point.

121Slide122

passion. innovation. accountability.

Sources

>

Appendix 5Slide123

Sources:- Beecher Carlson- www.air-worldwide.com- www.rms.com- www.msbinfo.com

- www.acord.org

- www.eqecat.com

www.marsh.com www.lmalloyds.com www.AmWins.com www.riskandinsurance.com

Deloitte Consulting AG NAPCO, LLC www.wgains.com

ASPERTA Swiss Re Munich Re Insurance Journal www.acetempestre.com www.propertycasualty360.com Honor Construction Inspection Service

Harrison

, Connor. Reinsurance Principles and Practices, First Edition. Maryland: Insurance Institute of America, 2004.

- Duffy, Catherine. Held Captive, A History of International Insurance in Bermuda. Private, 2004.

Grossi, Patricia, and Kunreuther, Howard. Catastrophe Modeling: A New Approach to Managing Risk. New York: Springer, 2005.

The

information contained in

this presentation is intended as background information only. All information is provided "as is" with no guarantees of completeness, accuracy or timeliness and without warranties of any kind, express or implied.

Beecher Carlson is not responsible for, and expressly disclaims all liability for, damages of any kind, whether direct or indirect, consequential, compensatory, actual, punitive, special, incidental or exemplary, arising out of use, reference to, or reliance on any information contained herein.123