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# loss triangle philosophy - PowerPoint PPT Presentation

Loss Triangle Philosophy. Gary Blumsohn CARe Seminar: Cambridge, May 2008. Background. Committee on Reinsurance Research Practical questions: Actuaries mostly learn to do loss development on the job Can we give guidance to improve approaches – especially on unstable triangles?.

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### Loss Triangle Philosophy

Gary Blumsohn

CARe Seminar:

Cambridge, May 2008

• Committee on Reinsurance Research

• Practical questions:

• Actuaries mostly learn to do loss development on the job

• Can we give guidance to improve approaches – especially on unstable triangles?

• What types of averages do people use?

• Statistical tests and methods

• Smoothing

• Reversals

• Downward development

• Ignore tail-factor issue

• 12-year excerpt from RAA GL Fac

• Too stable!

• 40 responses

• Mean loss reserve estimate = \$1.6 billion

• SD of loss reserve estimates = \$0.2 billion

CV = 13%

Second Attempt:Umbrella incurred loss triangle

• “Great and gutsy project!”

• “I believe the whole notion of "picking factors" with no statistical guidance is something of a disgrace to the profession…”

• “While it may be helpful to share ideas on how to pick LDFs, it is vital that more information than just the triangle at hand be considered… I wouldn’t make selections without other information such as individual claim information, changes in the underlying business, comparison to competitor or industry triangles if available, etc. Of course you can’t always get the information you want……but I would hate to see people come to the seminar and learn some new selection techniques that don’t look beyond the triangle.”

“Actuaries must not pretend to judge what they cannot scientifically model.”

Leigh Halliwell

Variance, Vol. 1, Issue 2, p. 216

Skeptic’s view of statistical methods scientifically model.”

• Statistical methods measure the past

• You have how many data points?!!

• Blow-ups more likely to be from things that aren’t in the data than from 1-in-10,000 events.

Economic Perspective scientifically model.”

Complete determinism:

Know the future

Perfect

Knowledge

Stochastic determinism:

Know the future statistically

Risk

No determinism:

Don’t know distributions

Uncertainty

Blumsohn, PCAS 1999, p. 31

“If you cannot measure, scientifically model.”

and unsatisfactory.”

Lord Kelvin

The Dilemma scientifically model.”

Your knowledge is meager and unsatisfactory,