Transcript e-Commerce Risk - Casualty Actuarial Society

Quantifying e-Commerce Risk
David Fishbaum, FSA
Chuck McClenahan, FCAS
MMC ENTERPRISE RISK
CAS Seminar on Ratemaking - March, 2001
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The Problem
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You’re the risk manager of a financial institution
with a new web site
Your insurance broker has provided you a quote
for new e-commerce risk insurance coverage:
$350,000 - $450,000 with low limits
Your not exactly sure what the risks of the web
site are
What to do?
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Background
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The financial institution provides community
banks with a product portfolio of ancillary
products such as:
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investments (mutual funds and stock trading)
insurance
other banking services
You provide web sites for these community
banks for investments, insurance and lending
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What are the risks?
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Failure of the web site
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problems with the surroundings, power failure, fire or
flooding
failure of the hardware
failure of the software
attack through virus or computer hacker
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Resultant damages are
also varied
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Delay in performing a service
Loss of brand value due to unreliability of
service or transmission of computer virus
loss of value through failure to deliver
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for example, an uncompleted stock trade
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Background: E-commerce
insurance coverage
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There is an intensive application
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the problem is that you can’t figure out how complex
or risky a web site you are running
A system audit is part of the insurance coverage
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there is a bias to find fault
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How do you insure the high
P/E ratio
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Its 1999 and the price/earnings ratio of the ecommerce function seems to have broken down
The unspoken issue is how do you insure the
value lost if something happens to the web site?
Not sure this is an issue today
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Why bring in Actuaries?
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Looking for someone to quantify the risk
We brought a multidisciplinary team of
actuaries, economists and policy expert
The actuaries provided the quantification and
modeling skill sets
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Methodology
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Model the web site
Stochastic testing
Scenario testing
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Model
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MMC ER developed a computer program to
model the economic performance of the ecommerce infrastructure
Used company’s performance statistics
Used a Monte Carlo simulation to produce
expected revenue and branding values
Based on this quantification, valued the
potential losses of a series of scenarios
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Flow of Information and quantification of failure probabilities
ISP Provider
Application Server/Firewall/Proxy Layer
In our estimation of the probability of failure at the application host level, elements such as software outage, hardware outage,
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data base performance etc were considered.
Assumptions
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Visits per week
Usage over the week
Revenue
Customer value
Application acceptance
Downtime
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Results-Base Case
2000
2001
2002
# of participating banks
Internet applications
Application fees
Insurance underwriting
TOTAL
New loans to banks
Present value of income on
new loans
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The Scenarios
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Denial of service
Physical damage to hardware location
New virus brings down complete system
Malicious employee
Threats/extortion
Theft of credit card numbers
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The Scenarios
Denial of service
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Attack causes a degradation of performance or
loss of service to web site
Not covered under current coverage
Modeling assumption: site down for 3 hours
Income loss/Customer value loss
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The Scenarios
Physical damage to hardware location
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Location of where hardware is kept is disabled
Covered under current insurance
Modeling assumption: site down for 10 days
Income loss/Customer value loss
Client bank’s lost revenue
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The Scenarios
New virus brings down complete system
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Not covered under current coverage
Model assumption: system down for 2 days
Income loss/Customer loss
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The Scenarios
Malicious Employee
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Destruction of important data or programs
Cost of recovery process covered under current
coverage
Not modeled
Theft of policyholder info or other intangible
property
Not covered under current coverage
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The Scenarios
Threats/extortion
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Threat to commit a computer crime or to use
information gained from a computer crime in
exchange for money, personal gain or to
embarrass the company
Would be covered under current kidnap and
ransom policies
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The Scenarios
Theft of credit card numbers
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CD universe and Salesgate (e-mall)
No credit card numbers are stored
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Results of analysis
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Biggest risk business interruption
Third party loss is minimal at this time
though in time the Internet will affect its
client relationship
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Conclusions
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Better quantification of risks
Better able to make a purchase decision
Other risk management decisions
What isn’t at risk is also important
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Postscript
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The website is still in operation
Strategy has been proven successful
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e-Commerce Risk
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Bruce Schneier - Secrets and Lies
(Wiley Computer Publishing, 2000)
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“The insurance industry does this kind of
thing all the time; it’s how they calculate
premiums. They figure out the annual loss
expectancy for a given risk, tack on some
extra for their operational costs plus some
profit and use the result”
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e-Commerce Risk
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Bruce Schneier - Secrets and Lies
(Wiley Computer Publishing, 2000)
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“Of course there’s going to be a lot of
guesswork in any of these; the particular risks
we’re talking about are just too new and too
poorly understood to be better quantized
(sic).”
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e-Commerce Risk
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Pricing e-Commerce Risk
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Determine Strategy
Identify the Risks
Collect Available Data
Develop Model
Price According to Strategy
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e-Commerce Risk
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Determine Strategy
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“Guess and Confess”
Loss Leader
Self-Supporting
Franklin Approach
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e-Commerce Risk
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Determine Strategy - “Guess and Confess”
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Insurer uses best available judgment (usually
discovered deep in the bowels of the
marketing department) as to the proper rate
Alternatively, rely on advice of career agents
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e-Commerce Risk
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Determine Strategy - Loss Leader
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Aptly named, this strategy is based upon the
assumption that the best way to develop
experience and expertise is to write a lot of
exposure
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e-Commerce Risk
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Determine Strategy - Self-Supporting
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Goal is to cover losses and expenses,
including start-up expenses, over some
reasonable period of time. This is a radical
strategy and has rarely been adopted in the
property-casualty industry.
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e-Commerce Risk
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Determine Strategy - Franklin Approach
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Focuses on loss avoidance
Underwrites against “undesirable” hazards, e.g.
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large user base
large asset base
high public profile
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e-Commerce Risk
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Identify the Risks
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We have a good track record here
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Medical Malpractice
Computer Leasing
Asbestos and Environmental
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e-Commerce Risk
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How many do you recognize?
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Daemon
Data mining
Digital wallet
Extranet
Luhn formula
Smart card
Thin client
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e-Commerce Risk
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How many do you recognize?
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Daemon - a structured background process
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e-Commerce Risk
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How many do you recognize?
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Daemon - a structured background process
Data mining - looking for hidden data patterns
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e-Commerce Risk
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How many do you recognize?
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Daemon - a structured background process
Data mining - looking for hidden data patterns
Digital wallet - encryption software, user ID
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e-Commerce Risk
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How many do you recognize?
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Daemon - a structured background process
Data mining - looking for hidden data patterns
Digital wallet - encryption software, user ID
Extranet - authorized outsider-available intranet
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e-Commerce Risk
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How many do you recognize?
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Daemon - a structured background process
Data mining - looking for hidden data patterns
Digital wallet - encryption software, user ID
Extranet - authorized outsider-available intranet
Luhn formula - credit card verifying algorithm
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e-Commerce Risk
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Luhn formula
(1) Start with penultimate digit and, moving left,
double the value of each alternating digit. If you
get a two digit number, add the two digits.
(2) Add up all digits. Result must be zero mod 10
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e-Commerce Risk
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Luhn formula
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1234 567890 12347
1438 537790 14387
1+4+3+8+5+3+7+7+9+0+1+4+3+8+7=70
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e-Commerce Risk
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How many do you recognize?
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Daemon - a structured background process
Data mining - looking for hidden data patterns
Digital wallet - encryption software, user ID
Extranet - authorized outsider-available intranet
Luhn formula - credit card verifying algorithm
Smart card - personal electronic memory card
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e-Commerce Risk
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How many do you recognize?
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Daemon - a structured background process
Data mining - looking for hidden data patterns
Digital wallet - encryption software, user ID
Extranet - authorized outsider-available intranet
Luhn formula - credit card verifying algorithm
Smart card - personal electronic memory card
Thin client - network computer w/o hard drive
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e-Commerce Risk
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Ingram Micro Inc. vs. American
Guarantee & Liability Insurance
Company
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“The court finds that ‘physical damage’
is not restricted to the physical
destruction or harm of computer
circuitry, but includes loss of access,
loss of use and loss of functionality.”
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e-Commerce Risk
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Ingram Micro Inc. vs. American
Guarantee & Liability Insurance
Company
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“Restricting the policy’s language to
that proposed by American [i.e.that
contained in the policy] would be archaic.”
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e-Commerce Risk
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TD Waterhouse fined $225,000 for
repeated outages which left customers
unable to trade
11 online brokers reported 88 outages for
1st 9 months 1999 (12th firm reported so
many outages it didn’t keep track).
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e-Commerce Risk
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Collect Available Data
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Exposure base not well-defined
Economic costs of losses not disclosed
Industry is young and evolving
Threat base is also evolving
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e-Commerce Risk
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Collect Available Data
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Remember, “Lloyd’s List” was started in 1696
but it wasn’t until 75 years later that the
Society of Lloyd’s was formed
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e-Commerce Risk
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Develop Model
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Identify major processes
Identify major threats
Relate threats to processes
Determine (or guess at) parameters
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e-Commerce Risk
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Example - Distributed Denial of Service
(DDoS)
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e-Commerce Risk
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“Attack of the Zombies” - February,2000
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Monday, February 7
- Yahoo! portal rendered inaccessible for 3 hours
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Tuesday, February 8
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Buy.com 90% inaccessible
eBay incapacitated
CNN 95% inaccessible
Amazon.com slowed to 5 minute access time
Wednesday, February 9
- ZDNet.com unreachable
- E*Trade slowed “to a crawl”
- Excite 60% inaccessible
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e-Commerce Risk
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How DDoS Works
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Goal is to render system inoperable
One attacker controls multiple servers
Method: Break into numerous sites, install
“attack script” and orchestrate coordinated
attack
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e-Commerce Risk
HACKER
VICTIM’S
SERVER
UNWITTING
HOST
“ZOMBIE”
OTHER
NETWORK
COMPUTERS
USER PCs
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Hypothetical DDoS Costs
$25,000,000
Market Cap Loss
$20,000,000
Security Costs
Revenue Loss
$15,000,000
$10,000,000
$5,000,000
$0
1
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121
181
241
301
361
421
481
541
Minutes of Outage
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Hypothetical Cumulative DDoS Frequency
100.0%
80.0%
60.0%
40.0%
20.0%
0.0%
0
60
120
180
240
300
360
420
480
540
600
Minutes of Outage
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e-Commerce Risk
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Price According to Strategy
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Frequency will vary with
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Popularity
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Profile
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Potential
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e-Commerce Risk
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Price According to Strategy
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Severity will vary
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eToys v. E*Trade
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e-Commerce Risk
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“You gotta be careful if you
don’t know where you’re
going ‘cause you might not
get there.”
- Yogi Berra
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