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

2. Property Per-Risk: Basics Experience Rating (“burn cost”) Experience Rating (“burn cost”) Exposure Rating Exposure Rating “Layer” the overall premium “Layer” the overall premium Requires Insured Value profile and severity curve(s) Requires Insured Value profile and severity curve(s) Price other features Price other features Annual Aggregate Deductible Annual Aggregate Deductible Limited Reinstatements 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? Do we have information on a per- location basis? Are our pricing tools based on per- location data? Are our pricing tools based on per- location data?

6. Property Per-Risk: Problems & Solutions Problems: 1) No data on Blanket policies 2) Little detail in data for other policies 3) Prevalence of outdated curves 4) Poor price monitors 5) Disconnect over “PML”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 The majority of large risks are either blanket-rated or specifically-rated Blanket policies are in neither our pricing models or our TIV profiles 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 Per-location detail as included in Declarations Page Use ISO or Catastrophe Models as platform? 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 distinguish Building vs Contents Do not include Time Element Coverages Do not include Time Element Coverages Do not list deductibles Do not list deductibles Do not detail level of coverage Do not detail level of coverage All Perils vs Named Perils All Perils vs Named Perils Replacement Cost and Insurance-to-Value Replacement Cost and Insurance-to-Value “Ordinance or Law” provision for Time Element “Ordinance or Law” provision for Time Element

10. Property Per-Risk: Problem #2 Solution: Need data standard that includes more information Need data standard that includes more information Need pricing models to run on detailed file, not on “summarized” TIV profile 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 Current Data ISO PSOLD ISO PSOLD Company-specific, “proprietary” curves Company-specific, “proprietary” curves Outdated Data Outdated Data Lloyds Scales (source & date unknown) Lloyds Scales (source & date unknown) Ludwig (Hartford Ins Grp 1984-1988) Ludwig (Hartford Ins Grp 1984-1988) Salzmann (INA Homeowners, 1960) 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 Replace outdated models But show impact of new model! But show impact of new model! Incorporate other data sources Incorporate other data sources National Fire Protection Association (NFPA) National Fire Protection Association (NFPA) Size matters Size matters

14. Property Per-Risk: Problem #4 Lack of Consistency in Ceding Company Price Monitors Critical to experience and exposure rating Critical to experience and exposure rating Wide flexibility in charged premium due to discretionary pricing factors Wide flexibility in charged premium due to discretionary pricing factors Minimal info on Specifically-Rated risks 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 This is tough and requires discipline Double check: Double check: “First Principles” – OnLevel based on rate and price changes “First Principles” – OnLevel based on rate and price changes Historical comparison of average premium Historical comparison of average premium E.G., ISO MarketWatch 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” Internationally: “key location” U.S. Underwriters: “most likely loss amount given that a significant loss event has taken place” U.S. Underwriters: “most likely loss amount given that a significant loss event has taken place” U.S. Actuaries: 99 percentile (?) 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 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 severity as mix of “big” and “small” Define:PML = E[loss | “big”] 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 for Data Standard Need to make use of all available data Need to make use of all available data