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Cyber Insurance
The role of analytics in supporting the acceptance of cyber exposure
A presentation to C.A.S
By David Ovenden
Oct 2015
© 2015 Towers Watson. All rights reserved. Proprietary and Confidential. For Towers Watson and Towers Watson client use only.
Agenda

Introduction

Background

Coverage types

Actors

Vectors

Why is this a difficult class of business

The anatomy of a hack

How can analytics support
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Background








A few names that have had
security issues recently
Hacking is becoming an
increasingly mainstream
occurrence
The cost of data breaches is
escalating
The impact can be catastrophic
State actors are undoubtedly
involved
The subject matter is not well
understood within the insurance
industry
Our lives and business
interactions are digital
Insurance solutions lag behind
the issue
Source: Information is beautiful
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Some UK Government research
of large organisations
of large organisations
69%
90%
74%
38%
of small organisations
Had a security breach
Year on year increase
Were attacked by an unauthorised
outsider last year
Year on year increase
of large organisations
30%
of small organisations
of large organisations
39%
16%
of small organisations
Were hit by a DoS attacks in the last year
Year on year decrease
27%
of small organisations
Believe they have insurance that would
cover a breach
Year on year decrease
Source: UK Government Report on Information Security
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buster
thomas
tigger
Actors and Vectors
robert
soccer
batman

Vandals

Direct Hacks
test
pass

Thrill Seekers

Mass Attack
killer
hockey

Show offs

Infection / Virus
george
charlie

Thieves

DDOs
andrew
michelle
Social Engineering
love
Organised crime


Hacktivist

Inside jobs
jessica

Industrial espionage

Accidents
pepper

State craft

Poor practices

The unaware

Physical peril

sunshine
6969
daniel
access
123456789
654321
joshua
maggie
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starwars
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silver
password
123456
12345678
Coverage types

Underlying
Exclusion
First Party




Specialist
Coverage
1234
qwerty
12345
dragon
Loss, damage or corruption of data
Business interruption following a loss
Costs, fines and penalties
Reconstruction of data
pussy
baseball
football
Comprehensive
Data exclusions
Most products
exclude “cyber
cat”
letmein
monkey
696969

Third Party





Professional indemnity
Libel
General liability
Breach of privacy
D&O
abc123
mustang
michael
shadow
master
jennifer
111111
2000
jordan
superman
harley
1234567
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hunter
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trustno1
amanda
orange
biteme
Why is this so difficult?
freedom
computer
sexy
First Party
Third Party

Volume of activity

Understanding

Potential aggregations

Difficulty policing

Lack of knowledge

Poor history
thunder
nicole
ginger
heather
hammer
summer
corvette
taylor
austin

Difficult claims settlement

Limits required
1111
merlin



Lack of partnerships

environment
Limited pull or push
Limits required
Emerging legal

Lack of data
matthew
121212
golfer
cheese
princess
martin

Better opportunities?
chelsea
patrick
richard 7
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asdfgh
sparky
cowboy
Realistic analytical support
camaro
anthony
matrix

Understanding the data resources available

Setting the Data strategy

Pricing with limited data

Modelling appetite and disaster scenarios (EMLs)



falcon
iloveyou
bailey
guitar
jackson
purple
Underwriting
& Claims
Expertise
phoenix
aaaaaa
Catastrophe potential
morgan
tigers
Decision support
Portfolio management analytics
scooter
porsche
mickey
Analytical
Expertise
maverick
cookie
nascar
IT Security
Expertise
peanut
justin
131313
money8
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boomer
Building IP around Cyber within your organisation

Bring together a range of external data sources relevant for your target
segment, potentially including breach data segmented by:









Automation v Security Surveys



Underlying technology
Industry sector
Suppliers
Size / scale
Sensitivity of the data
Policy limits selected
Credit score
Cross reference physical security
Unless you have big data (unstructured data) expertise collect as much
objective structured data as possible from your IT partners
Work closely with your underwriters to ensure analytical rigour
If you have a portfolio of this risk, make sure it is actively managed
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An example commercial analytics environment
Integrated Commercial
data environment
Outcomes
Quote
engine(s)
Market data (CMA)
Policy MI
Performance data
Client data
Claims MI
Broker data
Interactive case level
decision support
UW and
Pricing
Analytics
Radar
Decision support
Industrialised technical reporting
Portfolio Analytics
External Financial Data
External Cyber data sources
Augmented
renewal lists
Interactive
Product
dashboard
Interactive
Regional
dashboard
Interactive
Broker
dashboard
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Questions?
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© 2015 Towers Watson. All rights reserved. Proprietary and Confidential. For Towers Watson and Towers Watson client use only.