2. Example…
◇ Fact: virtually every insurance company selling “cat”
coverage is guessing. Those who are underwriting
cyber risk are REALLY guessing.
◇ Observation: Most buyers of insurance will buy the
lowest-cost alternative
3. Example…
■ An insurance company that is guessing is very likely to over-price
their quote. If you truly don’t know your expected claims, you are
going to add a BIG cushion to the premium.
■ An insurance company empowered with the tools to calculate a
rational premium for this specific client makes a lower quote.
■ The empowered carrier will get the average or lower-risk client.
◇ A buyer with average or below-average risk asks for quotes:
4. Example…
■ An insurance company that is guessing is very likely to give them
the same quote.
■ An insurance company empowered with the tools to calculate a
rational premium for this specific client makes a higher quote.
■ The guessing carrier will get the higher-risk client.
◇ A buyer with above-average risk asks for quotes:
5. This is Classic “Adverse
Selection”
Adverse Selection + Darwin = a long and
profitable future for the empowered carrier
6. Our Current Focus
◇ Cyber risks
◇ System and informational risks in hospitals
◇ Smart tools to help a company or organization better
understand its vulnerability, most cost efficient ways to
reduce that vulnerability, help an enterprise prepare for
an event, and when it comes – as it will – to manage that
breach to minimize the financial and reputational risk of a
company.