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6 August 2018| CIPR Newsle er

A C M I R A

By Anne Obersteadt, CIPR Senior Researcher

The author is grateful to Karen Clark of KCC and Ma Nielsen and

Kay Cleary of RMS for their contribu ons to the ar cle and help

improving it with their insigh ul comments.

I We have all heard and seen how technology is reshaping

the insurance sector. When considering this, most of us

think of connected cars, blockchain and other hot tech

trends frequently found in headlines. But new technologies

have also given rise to sophis cated catastrophe models

(cat models). Advances in compu ng capabili es is the

most obvious growth contributor. Cat models came into

existence just over three decades ago, when personal com‐

puters were in their infancy. Their adop on transformed

the industry’s ability to assess and manage catastrophe

losses. Instead of mapping exposure with tacks on a map,

insurers could now generate loss scenarios based on geo‐

graphic and historical data.

Models have consistently expanded since their introduc on

to incorporate lessons learned from catastrophic events.

Over the past decade, the monumental leap in technology

and higher resolu on exposure data has accelerated this

evolu on. Models have become more complex and mul ‐

faceted as a result. Considering the data‐driven nature of

the insurance industry, it is easy to imagine how this trend

will likely con nue with the next wave of technology.

This ar cle will explore how advancements in technology

and learnings from model failures have improved modeled

outputs. It includes interviews with catastrophe risk model‐

ing specialist Karen Clark and meteorologist and geogra‐

pher Ma Nielsen. Ms. Clark is the chief execu ve officer

(CEO) of Boston‐based catastrophe modeling firm Karen

Clark & Co. (KCC). Mr. Nielsen is senior director, global gov‐

ernmental and regulatory affairs at Risk Management Solu‐

ons (RMS).

I A C M Cat models are an integral part of the insurance industry.

However, few would have predicted this when Ms. Clark

launched the first catastrophe‐modeling business, Applied

Insurance Research (AIR), in 1987. AIR models used newly

available computer so ware to create event and loss simu‐

la ons for hurricane risk. Simula ons were based on histori‐

cal natural hazard data and geographic informa on systems

(GIS). GIS is a framework designed to capture, manage and

analyze geographical data.

According to Ms. Clark, only a handful of reinsurers were

interested in these new models because they were showing

much higher loss poten al than insurers’ es mates. So the

industry con nued to rely on the maximum foreseeable loss

(MFL) formula to es mate catastrophe losses. The MFL is

the aggregate premium mul plied by a region‐specific fac‐

tor. Five years later, Hurricane Andrew would change how

insurers view risk forever.

“Un l Hurricane Andrew struck southern Florida in 1992,

insurers had a hard me believing losses could reach this

high. Actual insured property losses totaled $15.5 billion—

an amount never seen before,” Ms. Clark said. “AIR put out

an insured loss es mate of $13 billion four hours a er Hurri‐

cane Andrew struck. Property Claims Service (PCS) es mat‐

ed $7 billion.”

Thirteen insurers became insolvent and many others strug‐

gled from Hurricane Andrew’s unprecedented losses. This

illustrated the industry’s reliance on short‐term experience

and simple, premium‐based ra os underes mated catastro‐

phe loss poten al. As a result, the insurance industry began

to transi on to probabilis c cat models for risk assessment.

Cat models provide a more realis c approach to loss analy‐

sis because they use decades of historical data. This data is

used to es mate probability distribu ons of event charac‐

teris cs to simulate poten al future events. These events

are then superimposed on insurer exposures to es mate

losses specific to insured por olios. According to Ms. Clark,

Hurricane Andrew spurred reinsurers to start requiring

county‐level exposure data from primary insurers for use in

the models. Ra ng agencies also began to ask insurers for

their 100‐year probable maximum loss (PML) model output

to calculate catastrophic risk tolerance scores.

The use of cat models expanded globally as reinsurance was

sold worldwide. Brokers started to license models to help

meet the insurance industry’s growing need for modeled

output. This expanded the use of cat models by enabling

even smaller insurer to access them. Many smaller insurers

today s ll get modeled numbers from their brokers.

M B The majority of insurers and brokers rely heavily on external

vendor models. A er Applied Insurance Research (AIR) en‐

tered the market in 1987, Risk Management Solu ons (RMS)

and EQECAT, Inc. entered in 1988 and 1994, respec vely.

These three firms have been the main providers of cat mod‐

els. Many reinsurers and larger insurers license mul ple (Continued on page 7)

August 2018 | CIPR Newsle er 7

A C M I R A (C )

in subsequent releases. Cat models also evolve over me to

reflect improvements in the understanding of the science of

a peril and its loss drivers. This sec on will take a more in‐

depth look at how models have evolved rapidly from les‐

sons learned since Hurricane Andrew.

2004 and 2005 Atlan c Hurricane Seasons

The 2004 and 2005 Atlan c hurricane seasons led modelers

to increase their assump ons regarding hurricane frequen‐

cy and insured losses. These seasons brought two consecu‐

ve years of record ac vity and losses. Ms. Clark said this

brought a new focus on the impact of aggregate losses from

mul ple hurricanes. Prior to this, modelers were more fo‐

cused on high severity, low frequency single event cata‐

strophic risk. “A er 2005, models focused on whether they

had the frequency of events per year assump on correct,”

she said.

The 2004 season included six major hurricanes, the most

notable being Hurricanes Charley, Frances, Ivan and Jeanne.

The 2005 season was the most destruc ve season on record

at the me. It included a record of 15 hurricanes, seven of

which were major hurricanes. The most notable hurricane

was Katrina, with more than $45 billion in insured losses.

(Continued on page 8)

models and adjust the model output to reflect their own

beliefs and experience.1

In 1997, the Federal Emergency Management Agency

(FEMA) released HAZUS, the first open (nonproprietary)

model for earthquake hazard. A er 2004, the model was

expanded to include modules for wind and flood hazards.

In 2014, Karen Clark & Co. (KCC) created open cat models

for re/insurers looking for greater transparency and flexi‐

bility. KCC provides modeling pla orms for hurricane,

storm surge flooding, severe convec ve storms and earth‐

quake. A consor um of 21 re/insurers and brokers also

launched the Oasis loss modeling framework. Oasis is an

independent, global, open framework for use by anyone

who wants to create a cat model.

L L R H Many of the advancements in cat models have actually

come as a result of their failings. Did the model appropri‐

ately represent the physical event? Were the assump ons

appropriate? How good is the model input data? Assess‐

ment of cat model performance following a catastrophic

event o en allows modelers to answer these and other

ques ons. Modelers can then address revealed deficiencies

W C M ?

Cat models are designed to quan fy the financial impact of a range of poten al future disasters and to inform users on where future events are likely to occur and how intense they are likely to be. Based on the es mated probability of loss, they then es ‐mate a range of insured losses. Primary insurers use cat modeling in underwri ng and pricing policies, managing claims, evalua ng risk‐transfer solu ons, deter‐mining appropriate capital levels and growing/limi ng exposures. They also use modeling results in regulatory rate filings, ra ng agency submissions, and shareholder and financial counterparty communica ons. Addi onally, cat models help reinsurers and reinsurance brokers determine the appropriate price and structure for reinsurance trea es. There are four basic modules to all cat models: event; intensity; vulnerability; and financial. The event module generates thou‐sands of possible catastrophic event scenarios based on a database of historical parameter data. The intensity module deter‐mines the level of physical hazard specific to geographical loca ons. The vulnerability module quan fies the expected damage from an event given its intensity and exposure characteris cs. The financial module measures monetary loss from the damage es mates for different policy condi ons. The primary metrics provided by a probabilis c catastrophe model are the exceedance probability (EP) curve, the probable maxi‐mum loss (PML) and the average annual loss (AAL). The EP curve is the annual probability a certain loss threshold is exceeded. The AAL is the average loss of the en re loss distribu on and is represented as the area under the EP curve. It is referred to as the “pure premium” because it is the amount of annual premium needed to cover modeled losses. The coefficient of varia on (CV) measures the uncertainty around the AAL es mates. Combined, these metrics provide important guidance to cat model users on the poten al frequency and severity of loss events.


A C M I R A (C )

Hurricane Katrina itself led to model innova ons in how so‐

called “super catastrophes” increase nonlinear loss amplifi‐

ca on, correla on and feedback. Unique to prior hurri‐

canes, Katrina resulted in more losses from secondary

flooding than the original wind generated catastrophe. The

secondary flooding was a result of severe storm surge and

levee failure. Models at the me did not include the poten‐

al for cascading consequences, such as storm surge, from

extreme events. They also did not include design risk for

poten al levee breakage. Inclusion of these elements in‐

crease loss es mates to property and me element cover‐

ages, such as business interrup on. 2

Katrina’s maximum sustained wind speeds of 140 mph re‐

sulted in a severe storm surge reaching over 30 feet and

toppling the levees protec ng New Orleans. As a result,

approximately 80% of New Orleans was flooded. Addi onal‐

ly, emergency response systems failed and a large number

of deaths occurred. RMS es mated the total footprint con‐

tained proper es valued at $1.5 trillion. Insurers pay only

the por on of property damage a ributable to wind. Losses

from water damage are typically covered by the Na onal

Flood Insurance Program (NFIP). The extensive damage sus‐

tained by both wind and water made it extremely difficult

to determine the root peril in many cases, leading to fre‐

quent li ga on.3

Mr. Nielsen said Hurricane Katrina illustrated the im‐

portance of represen ng flood defenses and the fact they

some mes fail in models for coastal flooding. “This is when

we really started looking at the severity of flood and surge

height on the Mississippi coast and protec on of life,” he

said. RMS also began to more explicitly determine where

the losses for wind and water intersected.

Hurricane Ike

Hurricane Ike was the most costly of six consecu ve storms

during the 2008 Atlan c hurricane season. The hurricane

began in Texas with winds extending far into the north once

it merged with an extra‐tropical storm. Losses escalated un‐

expectedly from damage in northern states with compound‐

ing losses from power outage, wind, surge and evacua ons.

Mr. Nielsen stated Hurricane Ike spurred RMS to make

model changes for higher inland wind speeds, storm surge

and greater building vulnerability. Construc on along the

Gulf Coast was found to be of lower quality than originally

es mated.4 Mr. Nielsen noted Hurricane Ike demonstrated

how far inland damage can occur and how fast winds decay

as storms move inland. “For these reasons, models showed

Ike weakening much faster than what actually occurred,”

he said.

Superstorm Sandy

Superstorm Sandy in 2012 was the second costliest Atlan c

windstorm. Superstorm Sandy brought record storm surge

to the Northeast, with the highest losses occurring in New

York and New Jersey. Similar to Hurricane Katrina, more

than 65% of insured losses from Superstorm Sandy were

from storm surge rather than wind. It should be noted not

all hurricane deduc bles triggered due to Sandy’s classifica‐

on as a superstorm rather than a hurricane.

Superstorm Sandy underscored the need to effec vely mod‐

el storm surge losses. Many of the storm surge models were

subsequently updated with new flood data from FEMA and

the NFIP. Superstorm Sandy also illustrated the importance

of high‐resolu on physically based numerical models in ac‐

curately assessing flood risk. Flood damage can vary consid‐

erably by specific loca on, with adjacent proper es experi‐

encing varying hazard. As such, lower‐resolu on models

using aggregate ZIP code‐level data do not provide sufficient

detail for underwri ng flood coverage.5

The Eastern seaboard experienced high winds, rains and

coastal flooding with Superstorm Sandy. As the system

reached the Appalachia and upper Midwest, it produced

significant snowfall. Severe power outages occurred in 15

states, resul ng in severe business interrup on.

Mr. Nielsen stated while modelers were not surprised by

Superstorm Sandy’s severity, they were surprised at the

level of business interrup on and infrastructure down me.

“Business interrup on was much worse than originally fore‐

casted, largely due to abundance of vital machinery and

contents in building basements—par cularly in lower Man‐

ha an,” he said.

Models underes mated commercial losses by not factoring

in the importance of basements. Basements are important

to flood risk because they can contain the majority of the

contents value in a building. Losses can escalate in the ab‐

sence of prompt post‐event cleanup. Mr. Nielsen stated

RMS changed how it looked at commercial exposure a er

Superstorm Sandy. In addi on to considering exposure loca‐

on, it also now considers how the exposure is distributed

within the building. RMS also calibrated models to take into

account how sensi ve the Northeast was to massive power

outages and infrastructure disrup on.

Hurricanes Harvey, Irma and Maria

Hurricanes Harvey, Irma and Maria were the most destruc‐

ve of the 2017 Atlan c hurricane season. Harvey was the



A C M I R A (C )

first to strike, followed by Irma and then Maria in the follow‐

ing weeks. The consecu ve hurricanes are es mated to have

accounted for about 60% of global insured losses in 2017.6

Like Katrina, Hurricane Harvey was a reminder on the im‐

portance of flood defenses in models for coastal flooding.

Harvey set the record for the most rainfall from a cyclone in

the con nental U.S. It produced excessive rainfall along the

Texas coast for four days in 2017. As such, most of the losses

were from rainfall flooding, not storm surge. Loss es ma on

was complicated by the lack of inland flood modules in the

hurricane models of two of the primary modeling vendors.

Addi onally, flood coverage data from reinsurance submis‐

sions were found to have gaps.

Mr. Nielsen said not all modelers, including RMS, had re‐

leased models yet for hurricane induced rainfall events.

“Harvey was another unseen record breaker and was the

most intense rainfall event in the U.S. This gave us the fur‐

ther push to create a tropical cyclone rainfall model,” he

said.

The RMS U.S. Flood Model quan fies flood risk by leveraging

high‐defini on technology to account for all sources of in‐

land flooding and antecedent condi ons. The model explicit‐

ly considers correla on with tropical cyclone wind and

surge. It also fills the gaps le by an incomplete levee data‐

base and leverages NFIP exposure data, business data and

field observa ons.

Hurricane Irma brought record sustained high wind speeds of

more than 180 miles per hour for 37 straight hours. Irma

started out as a Category 5 hurricane in the Caribbean, but

weakened before making landfall in Florida. The hurricane

was originally expected to hit Miami, which could have

caused record losses due to the city’s dense infrastructure.

This close call led modelers to look more closely at how much

higher loss es mates would have been if Hurricane Irma had

kept on its forecasted track.7

Hurricane Maria was one of the most destruc ve hurricanes

to hit Puerto Rico. The hurricane resulted in higher than

modeled business interrup on losses. This was due in part to

sustained power outages and other infrastructure issues. Cat

models typically calculate business interrup on as a func on

of property losses. This proved to not be accurate for Puerto

Rico’s situa on. Although the island’s many manufacturers

experienced significant business interrup on, they did not

experience significant property damage. 8

Modelers also took note of the addi onal me and cost

industries reliant on quality control incurred to restore

condi ons for inspec on and re‐cer fica on. Addi onal‐

ly, Hurricane Maria was a reminder of the importance of

emergency management and insurance coverage in post

‐event recovery. Only about half of homes had wind cov‐

erage, and poor economics and geographical loca on

made relief efforts difficult.9

Overall, hurricanes since Katrina have highlighted the

impact addi onal factors—such as demand surge, evac‐

ua on, sociological risks and poli cal influence—can

have on losses. Models are increasingly using combina‐

ons of economic and sociological modeling to incorpo‐

rate loss amplifica on from these addi onal factors. 10

M P A Advancements in hardware and so ware have brought

new modeling pla orms into existence. These new

models improve re/insurers risk assessment and provide

different views of risk. Among the changes are improved

exposure data, higher resolu on geophysical variables

and more complex algorithms. The remainder of this

ar cle will highlight how technology has influenced

model development.

Numerical Models

Modeling vendors began to use physically based model‐

ing technology, such as numerical weather predic on

(NWP), in 2000. NWP models were adapted from those

used in the meteorological community. NWP predicts

weather by combining weather observa ons with math‐

ema cal equa ons of the physics governing the atmos‐

phere. NWP has greatly enhanced the modeling of ex‐

treme events by simula ng an en re range of poten al

storm experience, including tail events. Advances in

compu ng power and mobile communica ons have

enabled NWP models to reach the level of high resolu‐

on needed to provide loca on‐specific forecasts.11

Mr. Nielsen said severe thunderstorms are a good ex‐

ample of the impact NWP has had on assessing loca on‐

specific risks. “In 2008, NWP models only had a resolu‐

on of 36 km, which was way too big to capture torna‐

does and hailstorms. Now they can reach 10‐20 meters

in resolu on. This provides us enough resolu on to

track where tornadoes go and e the hazard back to the

loca on with more certainty,” he said.



A C M I R A (C )

Near Term Models

The 2004‐2005 Atlan c hurricane season (and Hurricane

Katrina in par cular) led many modelers to ques on if hurri‐

canes were ge ng bigger and more frequent. This led to

vendors introducing both near‐term and long‐term event

frequency forecasts in 2006. The introduc on of near‐term

models varied significantly from the long‐term models tradi‐

onally used by the industry. Near‐term models were de‐

signed to predict events for the next five years. Their event

frequencies use forecasts of meteorological pa erns to cap‐

ture the influence of climate and oceanic condi ons on fre‐

quency, severity and loca on.

Near‐term models predicted a significant increase in hurri‐

canes compared to long‐term models. Insurers responded by

raising their rates to cover the es mated increase in losses.

As a result, insurance in parts of the coast became extremely

costly and market disrup on occurred. This raised transpar‐

ency concerns with regard to re/insurers’ and regulators’

abili es to understand the applica on of these new assump‐

ons in the models. Today, those modelers who use near‐

term models (referred to as medium term by RMS) offer

them as one of many views available to users.

Open Pla orm Technology

As former Microso CEO Steve Ballmer pointed out, “The No.

1 benefit of informa on technology is it empowers people to

do what they want to do.” Open models leverage newer

technology to do just this by allowing users direct access to

all model assump ons. They can then dig into the details to

fully understand and customize the model methodology to fit

their unique needs.

Ms. Clark understood the value of open models early. Alt‐

hough she never intended to build cat models again, she felt

compelled to address the limita ons of proprietary third‐

party models. “When the losses generated by a model

change drama cally, it can cause major industry disrup on,

par cularly when re/insurers cannot see or verify what’s

different in the updated model,” Ms. Clark said. “Given the

complexity of the models and because expert judgment is

frequently applied, there is considerable opportunity for

model assump ons to go wrong. With the tradi onal models,

when loss es mates go up and down between model ver‐

sions, re/insurers don’t know if the changes are jus fied or

caused by gaps in the model development process.”

Karen Clark & Co. (KCC) decided to overcome these limita‐

ons by building new open models designed to deliver addi‐

onal value to re/insurers. “Open models are the most signifi‐

cant change in the industry since the first models were

developed,” Ms. Clark said. “It’s a paradigm shi requir‐

ing advanced model architecture. KCC architected its

modeling pla orm so components would be more visual

and accessible to model users. You can’t do this with

legacy models and so ware.”

Ms. Clark stated KCC models use the same science, but

make every event intensity footprint and damage func‐

on visible and accessible to the user. KCC also allows

users to customize the models to their unique claims‐

handling processes and experience. This is important

because, as recent events have illustrated, varying

claims‐se ling prac ces can greatly impact modeled

loss amounts.

Demand by users for more transparent and flexible

models spiked in 2011 as a result of various develop‐

ments. The year saw record global natural catastrophe

losses. The largest losses came from earthquakes in Ja‐

pan and New Zealand, as well as flooding in Thailand

and Australia. Insurers were also surprised by significant

and unan cipated changes in model assump ons and

loss es mates from RMS Version 11. In addi on, the

new European regulatory Solvency II regime12 was ex‐

pected to bring changes to how insurers use models by

encouraging them to focus on developing their own

views of risk.13

“Some mes, when we have learnings in the models, it

can change results by a fairly substan al amount. This

can come as a shock to the insurance industry,” Mr.

Nielsen said. “So, over the past seven years, the indus‐

try has been doing a be er job understanding the un‐

certainty and sensi vity in modeled results. This move‐

ment has helped modelers become more transparent

about their assump ons.”

Addi onally, AIR and RMS are both jumping into the

open pla orm space. As part of RMS’ open pla orm

strategy, it is partnering with startups to build applica‐

ons for its RMS(one) exposure and risk management

pla orm. The pla orm can also be customized by RMS

clients, data providers and public sector developers.14

Ms. Clark sees open models becoming the norm in the

future, as users become more sophis cated and want

control of the model assump ons. She believes this is



A C M I R A (C )

because the difference between models is based on how

assump ons are implemented and not in the science itself.

“In 1995, the value in a model was in the proprietary nature

of it. This is because while the models were much less com‐

plex, catastrophe modeling was a new field that required

specialized exper se in science, engineering and so ware

development. Today, there are a lot more experts in re/

insurers who understand the science and the engineering.

So, the value is not in keeping secrets from the model users,

but in providing advanced open so ware pla orms that

empower users to see, verify, and customize what’s inside

the model,” she said.

Open models also allow for more efficient model valida on

and verifica on. “It may seem counterintui ve, but more

people working on a model means there are more opportu‐

ni es for human error and mistakes. Open models allow for

the more robust valida on process required given models

are more complex,” she added.

I A C B D

A F The bedrock of cat models has always been data. So, what

makes big data unique? Big data provides massive amounts

of structured and unstructured data. Through big data ana‐

ly cs, mixed mul dimensional variables can be synthesized

to provide new insights in real me. Cat models leveraging

big data analy cs have greater poten al to build detailed

risk models of more use to re/insurers.

When cat models first entered the scene, the internet was

barely known. However, the advent of the internet of

things (IoT) and the plethora of devices connected to it has

provided new sources of data. Modelers have begun to tap

into these new sources of data to expand their modelling

abili es. Addi onally, remote sensing and geographic infor‐

ma on systems (GIS) have improved data‐capture on ob‐

served physical property. GIS and remote sensing can help

infer building characteris cs at higher resolu on from ex‐

is ng informa on.

Con nued development in this area will help reduce uncer‐

tainty on building characteris cs. Addi onally, model expo‐

sure data and claims data collec on could see improve‐

ments as aerial imagery taken by drones, satellite imagery

and mobile data‐capture become more in use. The growing

informa on from these technologies should be er inform

engineering analysis and improve building vulnerability

assessment.

Con nued advances in compu ng capabili es have the

poten al to provide for more complex algorithms and

rapid computa ons. Risk mapping will also likely improve.

Addi onally, the growing sophis ca on of instruments to

collect weather data variables may help fill data gaps in

the models.

Mr. Nielsen said advancements in technology and a be er

observer network have already improved observa on

data. “For instance, use of radar and hail pads has given

us a be er visual hail footprint during an event. Prior to

this, RMS relied on human observa ons and would draw

circles around clusters of reports,” he added. The poten‐

al to fill data gaps may also lead to more modelers es‐

tablishing their own networks of measuring sta ons to

supplement government and academic data.

Modelers’ current focus is on reaching resolu ons high

enough to price insurance for an individual property’s

specific characteris cs. However, the resolu on of some

perils, such as flood, is s ll evolving. Most flood models

aim for enough resolu on to capture the most significant

drivers of flood depths and wind speeds at individual

loca ons.15

C Cat models have become an integral tool in the insurance

industry. Large losses from recent natural catastrophes

have illustrated the value in the industry’s current focus

on loca on‐level risk assessment. They have also demon‐

strated the usefulness of high‐resolu on models and high‐

quality exposure data in mapping loca on‐specific expo‐

sure. As such, the need for dynamic and transparent mod‐

els is likely to grow. This could provide growth opportuni‐

es for new pioneering modeling businesses, such as Ka‐

ren Clark & Co. (KCC) and KatRisk. KatRisk started in 2012

with a focus on air and flood. In 2016, it, partnered with

Munich Re to bring a personal lines inland flood insurance

product to market.

Advancing technology and the need for increasing granu‐

larity is also likely to spur the growth of startups to enter

the loca on‐based analy cs space. Two such examples

are Cape Analy cs and HazardHub. Cape Analy cs uses

machine learning to analyze aerial imagery on assets such

as homes. It then feeds informa on—such as square foot‐

age, roof type and changes in a home—to an insurer. Haz‐



A C M I R A (C )

ardHub translates huge amounts of geospa al digital data

into easy‐to‐use “scores” to provide a comprehensive view

of any property in the con nental U.S.

The future of cat modeling looks bright. Supercompu ng

power is expected to expand real‐ me modeling and so‐

phis cated simula ons. The matura on of machine learn‐

ing, ar ficial intelligence and cloud‐compu ng could bring

output to even greater resolu ons. This could bring model‐

ers’ abili es to understand and quan fy catastrophe risk to

new levels.

A A

Anne Obersteadt is a researcher with the NAIC Center for Insurance Policy and Research. Since 2000, she has been at the NAIC performing fi‐nancial, sta s cal and research analysis on all insurance sectors. In her current role, she has authored several ar cles for the CIPR News‐le er, a CIPR Study on the State of the Life Insur‐ance Industry, organized forums on insurance related issues, and provided support for NAIC

working groups. Before joining CIPR, she worked in other NAIC De‐partments where she published sta s cal reports, provided insur‐ance guidance and sta s cal data for external par es, analyzed insurer financial filings for solvency issues, and authored commen‐taries on the financial performance of the life and property and casualty insurance sectors. Prior to the NAIC, she worked as a com‐mercial loan officer for U.S. Bank. Ms. Obersteadt has a bachelor’s degree in business administra on and an MBA in finance.

12Solvency II sets out regulatory requirements for insurers in the Europe‐an Union addressing financial resources, governance, risk assessment and management, supervision, repor ng and disclosure. 13Whitaker, D. (2017). Natural Catastrophe Risk Management and Mod‐elling: A Prac oner's Guide, Open Modeling and Open Architectures Sec on. John Wiley & Sons. 14Robles, P. (2014, January 23). “RMS Adopts Open Pla orm Strategy for Risk Management SaaS, Unveils Commercial Partners.” Programmable‐Web. Retrieved from www.programmableweb.com/news/rms‐adopts‐open‐pla orm‐strategy‐risk‐management‐saas‐unveils‐commercial‐partners/2014/06/23. 15Jewson, S., Herweijer, C. & Khare, S. (n.d.). “Catastrophe Modeling for Climate Hazards: Challenges andClimate Change.” Insurance Informa on Ins tute. Retrieved from www.iii.org/sites/default/files/docs/pdf/RMS.pd ps://www.iii.org/sites/default/files/docs/pdf/RMS.pdf. 16Munich Re. (2016, June 21). “KatRisk Partnership” [Press Release]. Retrieved from www.munichre.com/us/property‐casualty/press‐news/news/2016/KatRisk‐Partnership/index.html. 17Knapp, A. (2018, April 2). This Data Startup Uses Ar ficial Intelligence To Figure Out If Your Roof Is In Decent Shape. Forbes. Retrieved from www.forbes.com/sites/alexknapp/2018/04/02/this‐data‐startup‐uses‐ar ficial‐intelligence‐to‐figure‐out‐if‐your‐roof‐is‐in‐decent‐shape/#5c7f39f159db. 18 h p://hazardhub.com.

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1PCS, a division of Verisk Insurance Solu ons, collects and aggregates insur‐ance industry loss data widely used by the insurance industry. 2“Oasis Consor um of 21 Insurers, Brokers Launches Independent Cat Mod‐el.” (2014, January 28). Insurance Journal. Retrieved from www.insurancejournal.com/news/interna onal/2014/01/28/318610.htm. 3Ibid. 4Marsh Insights. (2015, October). “A Decade of Advances in Catastrophe Modeling and Risk Financing.” Retrieved from www.oliverwyman.com/content/dam/marsh/Documents/PDF/US‐en/A%20Decade%20of%20Advances%20In%20Catastrophe%20Modeling%20and%20Risk%20Financing‐10‐2015.pdf. 5Risk Management Solu ons. (2013, October). Modeling Sandy: A High‐6Resolu on Approach to Storm Surge [White Paper]. Retrieved from h p://forms2.rms.com/rs/729‐DJX‐565/images/tc_2013_rms_modeling_sandy_storm_surge.pdf. 7Seria, J. (2018, March 12). “The Cost of Catastrophes” [Press Release]. Score Live Blog. Retrieved from www.scor.com/en/media/news‐press‐releases/cost‐catastrophes. 8Ibid. 9Ibid. 10Ibid. 11Jewson, S., Herweijer, C. & Khare, S. (n.d.). “Catastrophe Modeling for Climate Hazards: Challenges andClimate Change.” Insurance Informa on Ins tute. Retrieved from www.iii.org/sites/default/files/docs/pdf/RMS.pdf. Clark, K. (2002, April). “The Use of Computer Modeling in Es ma ng and Managing Future Catastrophe Losses.” The Geneva Papers. 27(2). Retrieved from www.air‐worldwide.com/_public/NewsData/000252/geneva_papers.pdf.


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