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.
8 August 2018| CIPR Newsle er
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
(Continued on page 9)
August 2018 | CIPR Newsle er 9
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.
(Continued on page 10)
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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
(Continued on page 11)
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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‐
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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.
20 August 2018| CIPR Newsle er
August 2018 | CIPR Newsle er 21
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