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EuropeNumber of Fellows + Associates in the US  23,000 Percent of Non-life actuaries - 19.0% Percent of Non-life actuaries in Reinsurance  12.1% Number of Non-life, Reinsurance actuaries in US  529 Number of Actuaries in Europe  18,930 Percent of Non-life actuaries  15% Percent of Non-life actuaries in Reinsurance  18.62% Number of Non-life, Reinsurance actuaries in Europe - 5296JReinsurance Results WAvoid the ValleysRate monitor Capture all business Produce regular reports Measure against exposure Adjust for Terms & conditions Changes in coverage Varying limits / retentions Underlying rate changes T R[ R[  !dAdjusted Loss Ratios `Adjusted Loss Ratios cAdjusted Loss Ratios bAdjusted Loss Ratios ]Avoid the ValleysUnderstand the risk Discussion with underwriters Discussion with cedents Incidental classes matter Read non-actuarial reports Underwriting submission Claims reports Trade pressJj3j26-  h ^Avoid the ValleysHLocate the floor Objective measurement Unbiased analysis Portfolio view @777 _Avoid the ValleystCheck for reasonable results Applies to both experience and exposure rating Include consideration of unknown risk :WWs VTesting XS Layer Projections>Assumptions Number of vehicles = 10 million Average premium = 1,000 Total subject premium = 10 billion Loss Ratio = 100% Selected XS Factor, above 1 million => 10% Total Loss Cost above 1 million = 1 billion Estimate an average excess loss cost, say => 2 million Then the implied claim count = 500 Is this reasonable?h n+ n+ KCelebrating Differences.KDifferent data available Different coverages Different pricing approachesLL&$ LDifferent Data AvailablefEUROPE In general, almost no market data available for Europe Exception: Germany Amount of client specific data varies by country Even in  good countries (UK), the data varies by company Standard Motor market questionnaire Rate changes - Rate change info provided by cedent, sometimes with supporting calcs(7  k $ T    ($  0  USA ISO collects data from all member companies Loss costs, exposure curves Amount of data varies by company Companies more open to reinsurer visits in the US Rate changes  Due to regulation, usually filed with state insurance departments, esp. personal lines-$2f  " 7.M 9 x[Different DataInsurance Service Organisations Data from member companies in US 90% of insurance premium in some lines Excess Loss Development ILF tools Very detailed info By state Rating class Coverage p (}    }:  MDifferent Coverages ~Motor (Auto) In US, basic limits quite low In Europe, unlimited liability exists Selby  Private motor claim Public Liability (General Liability) Limits in US $1m/$2m, then umbrella on top No umbrella in Europe, PL policies purchased to make total limit Employers Liability In US, Workers Comp, 1st party coverage, unlimited EL is 3rd party coverage, though standard of negligence has been eroded in many countries. In UK, standard limit of 10m D&lD    l   " p ZDifferent Pricing Approach With no market data, reinsurers rely more on experience Credibility of exposure rating is lower Experience Rating Issues Small data sets  analysis of small, regional business is volatile from year to year; don t overreact to random fluctuations Note  free cover ,  unused layer Make judgmental adjustment Fit distribution to limited losses, then extrapolatev8(Q8(Q  B    " 6NDifferent Pricing Approach :Exposure rating issues Reinsurers attempt to build their own exposure curves European  Countrywide data is much smaller data set than US countrywide data Data is not homogeneous* High layers still based heavily on judgement* Reliability of curves* *Some issues impact US curves also8#@    "\Different Pricing Approach General Issues Index Clauses Standard in Europe, rare in US Cover including/excluding ALAE Mainly a US issue Trend Unreliable information in some countries Reinsurance Contract Structuring)!  )      & OExposure rating DData set  loss triangles Can use data to estimate claims trend Get exposure data  premiums/ vehicle years Use exposures/losses to estimate frequency  poisson, neg bin Use loss size to estimate severity  pareto, lognormal Use distributions for non-standard layers Most loss severity data is not easily fit by curves How to get ultimate shape for casualty? 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fffTwcm___PPT10i.HL +D='  = @B +T '0 ~v  (   ~  s *%g X  `}  g    s *X &; <$@0    S QX  <$@0  H  0޽h ? fffTwcm~v___PPT10V.H +k'D*'  = @B D' = @BA?%,( < +O%,( < +D' =%(Dh' =%(D' =4@BBBB%(D' =1:Bvisible*o3>+B#style.visibility<* >%(D' =4@BBBB%(D' =1:Bvisible*o3>+B#style.visibility<* >Q%(D' =%(Dh' =%(D' =4@BBBB%(D' =1:Bvisible*o3>+B#style.visibility<* 1%(D' =4@BBBB%(D' =1:Bvisible*o3>+B#style.visibility<* 1M%(D4' =%(D' =%(D' =4@BBBB%(D' =1:Bvisible*o3>+B#style.visibility<* Q%(D' =%(Dh' =%(D' =4@BBBB%(D' =1:Bvisible*o3>+B#style.visibility<* %(D' =4@BBBB%(D' =1:Bvisible*o3>+B#style.visibility<* %(D' =%(Dh' =%(D' =4@BBBB%(D' =1:Bvisible*o3>+B#style.visibility<* Pq%(D' =4@BBBB%(D' =1:Bvisible*o3>+B#style.visibility<* q%(D' =%(Dh' =%(D' =4@BBBB%(D' =1:Bvisible*o3>+B#style.visibility<* 4%(D' =4@BBBB%(D' =1:Bvisible*o3>+B#style.visibility<* %(+ 30  d <(  d ~ d s *Qg X  `}  g  ~ d s *Rg X  YF g  H d 0޽h ? fffTwcm___PPT10i.H +D='  = @B + -0   h(   ~  s *g X  `}  g    s *g X 3<$@0 g  H  0޽h ? fffTwcm_W___PPT107.I9#+;Y"vD '  = @B D' = @BA?%,( < +O%,( < +D' =%(%(D' =%(D' =4@BBBB%(D' =1:Bvisible*o3>+B#style.visibility<* %(D' =4@BBBB%(D' =1:Bvisible*o3>+B#style.visibility<* +%(D' =4@BBBB%(D' =1:Bvisible*o3>+B#style.visibility<* +Q%(D' =4@BBBB%(D' =1:Bvisible*o3>+B#style.visibility<* Qm%(D' =%(D' =%(Dc' =4@BBBB%()))D' =1:Bvisible*o3>+B#style.visibility<* m%(D' =-o6Bwipe(up)*<3<* mD' =%(Dc' =4@BBBB%()))D' =1:Bvisible*o3>+B#style.visibility<* %(D' =-o6Bwipe(up)*<3<* D' =%(Dc' =4@BBBB%()))D' =1:Bvisible*o3>+B#style.visibility<* %(D' =-o6Bwipe(up)*<3<* D\ ' =%(D' =%(Dc' =4@BBBB%()))D' =1:Bvisible*o3>+B#style.visibility<* %(D' =-o6Bwipe(up)*<3<* D' =%(Dc' =4@BBBB%()))D' =1:Bvisible*o3>+B#style.visibility<* G%(D' =-o6Bwipe(up)*<3<* GD' =%(Dc' =4@BBBB%()))D' =1:Bvisible*o3>+B#style.visibility<* G%(D' =-o6Bwipe(up)*<3<* GD' =%( Dc' =4@BBBB%()))D' =1:Bvisible*o3>+B#style.visibility<* %(D' =-o6Bwipe(up)*<3<* + .0  h(   ~  s *g X  `}  g    s *g X s<$D0 g  H  0޽h ? fffTwcm___PPT10.I;+YD^'  = @B D' = @BA?%,( < +O%,( < +DP ' =%(D' =%(D' =4@BBBB%(D' =1:Bvisible*o3>+B#style.visibility<* az%(D' =%(Dg' =4@BBBB%()))D' =1:Bvisible*o3>+B#style.visibility<* z%(D' =-s6Bwipe(left)*<3<* zD' =%(Dg' =4@BBBB%()))D' =1:Bvisible*o3>+B#style.visibility<* %(D' =-s6Bwipe(left)*<3<* D' =%(Dg' =4@BBBB%()))D' =1:Bvisible*o3>+B#style.visibility<* 5%(D' =-s6Bwipe(left)*<3<* 5D' =%(pDg' =4@BBBB%()))D' =1:Bvisible*o3>+B#style.visibility<* 5j%(D' =-s6Bwipe(left)*<3<* 5j+ /0 0  h(   ~  s *@g X  `}  g    s *,g X e <$D 0 g  H  0޽h ? fffTwcmtl___PPT10L.I;+qZD ' = @B D' = @BA?%,( < +O%,( < +Dn' =%(D' =%(D' =4@BBBB%(D' =1:Bvisible*o3>+B#style.visibility<* M%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<* MD' =+4 8?dCB1+#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<* MDn' =%(D' =%(D' =4@BBBB%(D' =1:Bvisible*o3>+B#style.visibility<* M%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<* MD' =+4 8?dCB1+#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<* MD:' =%(D' =%(D' =4@BBBB%(D' =1:Bvisible*o3>+B#style.visibility<* %(D' =+4 8?dCB1+#ppt_w/2BCB#ppt_xB*Y3>B ppt_x<* D' =+4 8?\CB#ppt_yBCB#ppt_yB*Y3>B ppt_y<* D' =4@BBBB%())))?D' =1:Bvisible*o3>+B#style.visibility<* %(Dn' =%(D' =%(D' =4@BBBB%(D' =1:Bvisible*o3>+B#style.visibility<* %(D' =+4 8?dCB1+#ppt_w/2BCB#ppt_xB*Y3>B ppt_x<* D' =+4 8?\CB#ppt_yBCB#ppt_yB*Y3>B ppt_y<* Dn' =%(D' =%(D' =4@BBBB%(D' =1:Bvisible*o3>+B#style.visibility<* %(D' =+4 8?dCB1+#ppt_w/2BCB#ppt_xB*Y3>B ppt_x<* D' =+4 8?\CB#ppt_yBCB#ppt_yB*Y3>B ppt_y<* + 40  h(   ~  s *mg X  `}  g    s *g X <$D0 g  H  0޽h ? fffTwcmph___PPT10H.I;+{#jD'  = @B D' = @BA?%,( < +O%,( < +D'' =%(%(D' =%(D' =4@BBBB%())))?D' =1:Bvisible*o3>+B#style.visibility<* %(D' =%(D3' =4@BBBB%(D' =1:Bvisible*o3>+B#style.visibility<* <%(D' =-o6Bwipe(up)*<3<* <Dv' =%(D' =%(D3' =4@BBBB%(D' =1:Bvisible*o3>+B#style.visibility<* <[%(D' =-o6Bwipe(up)*<3<* <[D' =%(D3' =4@BBBB%(D' =1:Bvisible*o3>+B#style.visibility<* [m%(D' =-o6Bwipe(up)*<3<* [mDv' =%(D' =%(D3' =4@BBBB%(D' =1:Bvisible*o3>+B#style.visibility<* ms%(D' =-o6Bwipe(up)*<3<* msD' =%(D3' =4@BBBB%(D' =1:Bvisible*o3>+B#style.visibility<* s%(D' =-o6Bwipe(up)*<3<* sD' =%(D' =%(D3' =4@BBBB%(D' =1:Bvisible*o3>+B#style.visibility<* %(D' =-o6Bwipe(up)*<3<* + 20 @  <(   ~  s *8!r X  `}  r  ~  s *"r X DVj r  H  0޽h ? fffTwcm___PPT10i.K𶍭+D='  = @B +B0 @@ (  @X @ C dRH   r  @ S r d !  r  "H @ 0jB ? 3380___PPT10.^L]F0 x  (   d  c $dRH   r l  s *r d Y!  r  Slightly more actuaries in North America, which is SOA & CAS total Fellows + Associates BUT Some SOA and CAS actuaries work outside of North America, so doesn t truly reflect balance between US and rest of world X{ H  0jB ? ̙3380___PPT10.@J]G0   m(   d  c $dRH   g   s *Ĥd Y!  g  WAssumptions: Higher percentage of Life/Pensions actuaries in Europe (85% vs. 81%) More reinsurers based in Europe, so higher percentage of actuaries in Reinsurance Conclusion: same number of reinsurance actuaries in Europe and USA 6 L    H  0jB ? ̙3380___PPT10.+{xL0 x (   X  C dRH   g x  S ~d !  g  Germany  premiums, loss ratios. Slightly out of date. Not sure of source UK Motor  Fleets show number of policies rather than number of Vehicle Years. Veh years may not be provided even for non-fleet Postcode analysis for flood cover not consistent in UK. Hull values provided in Europe, reinsurance programs cover separately. QQ :K R H  0jB ? 3380___PPT10.hsLX0  \(   X  C dRH     S d !   ^Major assumption: percentage of non-life actuaries in Europe being slightly lower than US - 15% vs 19% Again, Based on total CAS + SOA fellows & associates, so some possibly working in Europe & bringing projections closer together. Not a big gap .a   H  0jB ? 3380___PPT10.BJ0 zr  (   X  C dRH   g r  S d !  g  H  0jB ? 3380___PPT10.pBNH0  ^(   X  C dRH     S Pd !   `From 7% of total population in 1990 to 13% in 2005, so reinsurers are recognizing value of actuaries to reinsurance activities If so many more actuaries involved in reinsurance, and many of them in pricing, why were 1997-2000 so underperforming in many markets, including USA?.7    H  0jB ? 3380___PPT10.p$50m loss, Austria tunnel >$30m (Mont Blanc  closed 3 years, hundreds of millions of pounds due to truck fire. >$100m, but not motor in the end ) Public Liability In US, companies tend to purchase separate Employers Liability In US, Employment Practices Liability also exists, for employment issues (wrongful termination, harassment, etc) @F    N ."    H  0jB ? 3380___PPT10.`hBZ0 zr  (   X  C dRH   g r  S g d !  g  rExperience rating issues similar in US, Europe, but European reinsurance contracts can be much smaller. Free cover  without exposure curves, more incentive to use experience. If largest claim is 4 million, how do you rate a 9m xs 1m layer? Fit 3 xs 1 and extrapolate using curve? Fit 4 xs 1? Add a 7m loss?::b    '   H  0jB ? 3380___PPT10.`hBN0 d\ $ (  $ X $ C dRH   g \ $ S |g d !  g  *Even in US, with larger data set, data reporting is not same for every company. High layers are still based on judgmental curve fitting. So how reliable are the curves? H $ 0jB ? 3380___PPT10.`hBW0 zr0(  (  ( X ( C dRH   g r ( S g d !  g  H ( 0jB ? 3380___PPT10.`hBV0 me@, (  , X , C dRH   g e , S pd !  g  If observed claims between 300-400 per year, after devt, inflation, maybe ok If observed 50-100 claims per year, probably too high. Revisit assumptions .3  b H , 0jB ? 3380___PPT10. C|O0 P0 (  0 X 0 C dRH   g  0 S  g d !  g  (ILF vs ELF Increased Limit, or Excess Loss factor Long tail  how to adjust current values at different maturities for ultimate curve selection.   H 0 0jB ? 3380___PPT10. C[0 0h (  h ^ h S dRH   g  h c $eg d !  g  9ISO provides many services of value to insurance industry:: 9 H h 0jB ? 3380___PPT10.hs\0 ~ (   ^  S dRH   g x  c $Pg d !  g  H  0jB ? 3380___PPT10.`hB]0 ~ (   ^  S dRH   g x  c $d !  g  H  0jB ? 3380___PPT10.`hBO^0  _(   ^  S dRH   g   c $d !  g  U%Avoid tweaking factors to meet target% H  0jB ? 3380___PPT10.`hB_0 *" (   ^  S dRH   g   c $ d !  g  \Reasonable results Even if experience & exposure are close, not indication of accuracy Unknown, inestimable risks  Product liability most obvious example Newly compensable injuries  stress due to worry about asbestos exposure Imposition of lower discount rate for claims calculations (Ogden) /- . H  0jB ? 3380___PPT10.`hBT`0 p d(   X  C dRH     S |~d !   f6Reasonable loss ratios for a casualty line of business6 H  0jB ? 3380___PPT10.1~c0  (   X  C dRH     S HXd !   `Adjusted for Underlying Rate Changes AND Reinsurance Rate Changes RI changes difficult if you don t understand the exposures. Often use premium, assume Underlying rates NOTICE  1. Somewhat flatter line. 2. Loss ratios in most recent years adjusted to approx 2007 level, so could be reasonable.11 0 H  0jB ? 3380___PPT10.1~8b0  H(   X  C dRH   g   S hd !   JT&C used as a basis for other soft factors. Changes in underlying coverage, or Reinsurance terms. Restrict/allow more territories. Widening of underlying pricing guidelines. If EL, impact of adding type of manufacturer. NOTICE  1. Loss ratios even flatter 2. Most recent 4 years much higher than 2007, so possibly underestimating 2007. 3. Higher rate changes required?   H  0jB ? 3380___PPT10.1~d0 F> (   X  C dRH   >  S t1d !   XT&C 2000  restricted territory  no incidental US business 2002  coverage restriction  no sport injuries 2005  sports injuries again 2006  incidental exposure in Africa Limits  increase width of reinsurance program, moving from 9 xs 1 to 13 xs 2 or something Can model using distributions-- H     . H  0jB ? 3380___PPT10.#&r u)mo :^mt0^Ġ`lu x= ptU W   "%e*)/+  1-/$2629=!CBG"1Oh+'0U  $ 0 <HPCulture Clash Doug LacossblankDoug50Microsoft PowerPoint@P0R@ @@ش-GSg  )'    """)))UUUMMMBBB999|PP3f333f3333f3ffffff3f̙3ff333f333333333f33333333f33f3ff3f3f3f3333f33̙33333f333333f3333f3ffffff3f33ff3f3f3f3fff3ffffffffff3ffff̙fff3fffff3fff333f3f3ff3ff33f̙̙3̙ff̙̙̙3f̙3f333f3333f3ffffff3f̙3f3f3f333f3333f3ffffff3f̙3f3ffffffffff!___www4'A x(xKʦ """)))UUUMMMBBB999|PP3f3333f333ff3fffff3f3f̙f3333f3333333333f3333333f3f33ff3f3f3f3333f3333333f3̙33333f333ff3ffffff3f33f3ff3f3f3ffff3fffffffff3fffffff3f̙ffff3ff333f3ff33fff33f3ff̙3f3f3333f333ff3fffff̙̙3̙f̙̙̙3f̙3f3f3333f333ff3fffff3f3f̙3ffffffffff!___wwwýÚÚàÚýÚzzzYzzàYzYYàzYàzYÚzÚàYàzzzàYzYàYz^zzÚzYzzzYààààz8Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y88^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8888Y7888888Y7888888Y7888888Y7888888Y7888888Y7888888Y7888888Y7888888Y7888888Y7888888Y7888888Y7888888Y7888Y8^8Y8Y8Y8^8Y8Y8Y8^8Y8Y8Y8^8Y8Y8Y8^8Y8Y8Y8^8Y8Y8Y8^8Y8Y8Y8^8Y8Y8Y8^8Y8Y8Y8^8Y8Y8Y8^8Y8Y8Y8^8Y8Y8Y8^8Y8Y88Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y888Y88^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y8^8Y878888888788888887888888878888888788888887888888878888888788888887888888878888888788888887888888878888888՜.+,D՜.+,\   $,4 < On-screen Show Converiumfs Arial Wingdings GaramondTimes New Roman Wingdings 2BlankEdgeCulture Clash: US v ThemNumber of Qualified ActuariesSlide 3Actuaries in Reinsurance%Reinsurance Actuaries US v. EuropeReinsurance ResultsAvoid the ValleysAdjusted Loss RatiosAdjusted Loss RatiosAdjusted Loss RatiosAdjusted Loss RatiosAvoid the ValleysAvoid the ValleysAvoid the ValleysTesting XS Layer ProjectionsCelebrating DifferencesDifferent Data AvailableDifferent DataDifferent CoveragesDifferent Pricing ApproachDifferent Pricing ApproachDifferent Pricing ApproachExposure rating  Fonts UsedDesign Template Slide TitlesP :B_PID_LINKBASEA_HDougDoug  !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~      !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~Root EntrydO)PicturesL*Current UserSummaryInformation(0UPowerPoint Document(IDocumentSummaryInformation8Root EntrydO)V1@PicturesL*Current User>SummaryInformation(0U"_H lwieczoreklwieczorek