1 / 21

Property Per-Risk Pricing Current Challenges

Property Per-Risk Pricing Current Challenges. David R. Clark American Re-Insurance Company CAS Seminar on Reinsurance; June, 2003. Property Per-Risk: Basics. Experience Rating (“burn cost”) Exposure Rating “Layer” the overall premium Requires Insured Value profile and severity curve(s)

jolanta
Télécharger la présentation

Property Per-Risk Pricing Current Challenges

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Property Per-Risk PricingCurrent Challenges David R. Clark American Re-Insurance Company CAS Seminar on Reinsurance; June, 2003

  2. Property Per-Risk: Basics • Experience Rating (“burn cost”) • Exposure Rating • “Layer” the overall premium • Requires Insured Value profile and severity curve(s) • Price other features • Annual Aggregate Deductible • Limited Reinstatements

  3. Property Per-Risk: Basics So we’re done, right?

  4. Property Per-Risk: A Preliminary Problem What is a “risk”? Typical Treaty wording: “The [ceding] Company shall be the sole judge of what constitutes one risk…” (subject to conditions) “Risk” can be decided after loss occurs!

  5. Property Per-Risk: A Preliminary Problem Roughly, “risk” = “location” • Do we have information on a per-location basis? • Are our pricing tools based on per-location data?

  6. Property Per-Risk: Problems & Solutions Problems: • No data on Blanket policies • Little detail in data for other policies • Prevalence of outdated curves • Poor price monitors • Disconnect over “PML”

  7. Property Per-Risk: Problem #1 Blanket Policies often not captured on a per-location basis • The majority of large risks are either blanket-rated or specifically-rated • Blanket policies are in neither our pricing models or our TIV profiles

  8. Property Per-Risk: Problem #1 Solution: Need a data standard that includes blanket policies • Per-location detail as included in Declarations Page • Use ISO or Catastrophe Models as platform?

  9. Property Per-Risk: Problem #2 Lack of Detail in Insured Value Profiles • Do not distinguish Building vs Contents • Do not include Time Element Coverages • Do not list deductibles • Do not detail level of coverage • All Perils vs Named Perils • Replacement Cost and Insurance-to-Value • “Ordinance or Law” provision for Time Element

  10. Property Per-Risk: Problem #2 Solution: • Need data standard that includes more information • Need pricing models to run on detailed file, not on “summarized” TIV profile

  11. Property Per-Risk: Problem #3 What severity curve is used? • Current Data • ISO PSOLD • Company-specific, “proprietary” curves • Outdated Data • Lloyds Scales (source & date unknown) • Ludwig (Hartford Ins Grp 1984-1988) • Salzmann (INA Homeowners, 1960)

  12. Property Per-Risk: Problem #3 Why Size Matters: Derived from proprietary American Re-Insurance study based upon customized ISO data.

  13. Property Per-Risk: Problem #3 Solution: • Replace outdated models • But show impact of new model! • Incorporate other data sources • National Fire Protection Association (NFPA) • Size matters

  14. Property Per-Risk: Problem #4 Lack of Consistency in Ceding Company Price Monitors • Critical to experience and exposure rating • Wide flexibility in charged premium due to discretionary pricing factors • Minimal info on Specifically-Rated risks

  15. Property Per-Risk: Problem #4 Principle: Rate and Price changes are explanatory variables for movement in loss cost. Consequence: We need to test how well they explain that movement.

  16. Property Per-Risk: Problem #4 Solution: • This is tough and requires discipline • Double check: • “First Principles” – OnLevel based on rate and price changes • Historical comparison of average premium • E.G., ISO MarketWatch

  17. Property Per-Risk: Problem #5 Difficulty in including Underwriters’ expertise What is a PML?

  18. Property Per-Risk: Problem #5 “The term ‘PML’ or ‘probable maximum loss’ is one of the most widely used terms in property insurance underwriting. But it represents one of the least clear concepts in all insurance.” John McGuinness - Is “Probable Maximum Loss” (PML) a Useful Concept?; PCAS 1969

  19. Property Per-Risk: Problem #5 PML is still an ambiguous concept: • Internationally: “key location” • U.S. Underwriters: “most likely loss amount given that a significant loss event has taken place” • U.S. Actuaries: 99 percentile (?)

  20. Property Per-Risk: Problem #5 Solution: Follow concept of U.S. Underwriters • Divide the world into “big” and “small” losses (small losses <1% of TIV are 75% of counts) • Define severity as mix of “big” and “small” • Define: PML = E[loss | “big”]

  21. Property Per-Risk: Conclusions Property Per-Risk Pricing is not a solved problem. Towards a solution: • Need for Data Standard • Need to make use of all available data

More Related