Blowing through the range 2014 CLRS presentation September 15, 2014 Christopher Andersen, FCAS, MAAA Ron Fowler, FCAS, MAAA
Agenda ► Actuarial ranges and their context ► Communication of the actuarial range ► When things go wrong – Case studies ► Financial guarantee ► Catastrophes ► Asbestos Page 2
What is in an actuarial range? ► Actuarial standards of practice No. 43 ► Property / Casualty Unpaid Claim Estimates ► Actuarial central estimate ► An estimate that represents an expected value over the range of reasonably possible outcomes (“range”). ► 3.3.1 …may not include all conceivable outcomes, as, for example, it would not include conceivable extreme events where the contribution of such events to an expected value is not reliably estimable. Page 3
What disclosures are proper for an actuary to communicate? ► 3.6.7 – External conditions ► Claim obligations are influenced by … potential economic changes, regulatory actions, judicial decisions, or political or social forces. …the actuary is not required to have detailed knowledge of or consider all possible external conditions… ► 3.6.7 – Changing conditions ► The actuary should consider whether there have been significant changes in conditions …Examples include reinsurance program changes, … claims personnel [changes], … Page 4
What disclosures are proper for an actuary to communicate? [cont.] ► 3.6.8 – Uncertainty ► The actuary should consider the uncertainty … This standard does not require or prohibit the actuary from measuring this uncertainty. … the actuary should consider the types and sources of uncertainty … [and] may include uncertainty due to model risk, parameter risk, and process risk. ► 4.2 – Additional disclosures (re: range of estimates) ► In the case when the actuary specifies a range of estimates, the actuary should disclose the basis of the range provided Page 5
When things go wrong – Case studies ► 2008 financial crises ► Financial guarantee insurance ► Unusual catastrophes ► Hurricane Katrina ► Thailand flooding ► New Zealand earthquake ► Asbestos Page 6
The 2008 financial crises was more sudden than previous depressions in the financial markets DJIA – Year over year percentage change 30.0% 20.0% 10.0% 0.0% -10.0% -20.0% -30.0% -40.0% Prepared by EY Page 7
Industry loss ratio spiked to 200% to 300% and have still not recovered to pre-financial crisis levels Loss ratios for Industry and Top Financial Guarantee and Mortgage-Back Securities writers 1000.0% 900.0% 800.0% 700.0% 600.0% 500.0% 400.0% 300.0% 200.0% 100.0% 0.0% P&C Industry Company 1 Company 2 Company 3 Company 4 Based on SNL data; Prepared by EY Page 8
Unprecedented financial market performance underlined inadequate reserve amounts in financial guarantee and mortgage guarantee products 1 year reserve development / Prior year reserves 125.0% 100.0% 75.0% 50.0% 25.0% 0.0% -25.0% -50.0% Commercial Auto Workers Compensation Medical Professional Fidelity and surety Financial Guarantee and Mortgage Gaurantee Based on SNL data; Prepared by EY Page 9
Financial guarantee insurance ► What went wrong? ► Was the cause of the reserve development due to process risk or parameter risk? ► The development was due to a “tail event” or process risk ► How did the actuarial models uphold? ► New information needed to be reflected that was not traditional to historical analysis and development Economic projections ► TARP program ► ► Scenario testing is critical in developing a range of expectations Page 10
2006 vintage curves indicated stability 50% 45% 40% 1998 35% 1999 2000 30% Severity 2001 25% 2002 20% 2003 15% 2004 10% 2005 2006 5% 2007 0% 2 8 14 20 26 32 38 44 50 56 62 68 74 80 86 92 98 104 110 116 122 128 Loan Age Page 11
Post 2008 financial crisis the historical development was no longer relevant 50% 45% 40% 1998 35% 1999 2000 30% Severity 2001 25% 2002 20% 2003 15% 2004 10% 2005 2006 5% 2007 0% 2 8 14 20 26 32 38 44 50 56 62 68 74 80 86 92 98 104 110 116 122 128 Loan Age Page 12
Financial guarantee insurance Case study – Post-crisis estimates and range ► Scenario testing was employed as future state after shock was difficult to ascertain from historical data ► What movement in transition probabilities is plausible? ► How high can loss severities be? Scenarios Transition probabilities Loss Severities Difference to Median A No change Decrease by 5% -36% B Used 1 month look back No change -28% C Used 2004 probabilities Increased by 5% -4% D Lag vintage 1 year Increased by 5% +4% Used 2005 probabilities for all E Increased by 7.5% +57% older vintages Used 2007 probabilities for all F No change +118% vintages Page 13
Developing a range using non-traditional information and scenario testing ► Scenario testing ► Search for relevant information from non-traditional sources Economic data and trends ► Understand reform and its impact ► ► Discuss with client reasonability of assumptions and apply professional skepticism to avoid biases Develop a maximum probable loss scenario ► Consider industry perspective on phenomenon ► Page 14
When things go wrong – Case studies ► 2008 financial crises ► Financial guarantee insurance ► Unusual catastrophes ► Hurricane Katrina ► Thailand flooding ► New Zealand earthquake ► Asbestos Page 15
Recent catastrophes have proven to have stipulations that make predicting ultimate losses more uncertain ► Hurricane Katrina ► Wind versus water disputes ► Extended recovery and rebuilding period ► Thailand flooding ► Claim investigation was severely delayed to due standing water and the inability to investigate claim sites ► Largest claims were business interruption and have a time- element ► New Zealand earthquake Page 16
The Thailand Flooding claims have been harder to identify and investigate due to the lingering water in warehouses 100.0% 90.0% % Case incurred to ultimate 80.0% Significantly lower after 6 periods ► 70.0% Time-element coverage is causing Thailand Floods ► uncertainty 60.0% Katrina 50.0% Rita Wilma 40.0% NZ2 EQ 30.0% Japan EQ 20.0% WTC 10.0% 0.0% 1 3 5 7 9 11 13 15 17 19 Periods Prepared by EY; based on RAA data. 1 period is equal to 3 months Page 17
Historical earthquake % of case incurred to ultimate development 120.0% The case incurred amounts continue to ► rise through 9 report periods % Case incurred to ultimate 100.0% NZ EQ2 80.0% Chile 60.0% Japan Loma Prieta 40.0% Northridge NZ EQ1 20.0% NZ EQ3 0.0% 1 2 3 4 5 6 7 8 9 1011121314151617181920 Periods Prepared by EY; based on RAA data. 1 period is equal to 3 months Page 18
New Zealand earthquake has experience high “event creep” than other recent natural catastrophes ► Event creep (New Zealand) ► New Zealand Earthquakes: Christchurch, NZ NZ I: October 2010 ► NZ II: February 2011 ► Many smaller aftershocks ► ► New Zealand Earthquake Commission pays up to 100,000 NZD property, 20,000 NZD contents ► Suncorp and IAG have majority of the market share for additional insurance ► Reinsurers then provide excess cover (they get the effects of the creep) Page 19
Reason for event creep for New Zealand earthquake ► Scope of event (largest natural CAT year in NZ) ► Apportionment: losses were apportioned between the two events, often with complicated models ► Renewals: the two earthquakes are treated as two events and renewals separate the contracts so there are new limits ► Liquefaction (some neighborhoods abandoned), this was not factored into many loss models ► Claim settlement time (government insurer handling claims, slower than private insurance to process) ► Cordoned off areas (Red Zone) causes BI losses Page 20
Liquefaction
When things go wrong – Case studies ► 2008 financial crises ► Financial guarantee insurance ► Mortgage-back security insurance ► Unusual catastrophes ► Hurricane Katrina ► Thailand flooding ► New Zealand earthquake ► Asbestos Page 23
A history of predictions and re-predictions ► The poor track record of estimating asbestos Manville Trust – Asbestos Claims filing 1,000,000 900,000 800,000 700,000 600,000 500,000 400,000 300,000 200,000 100,000 0 1988 1993 1997 2005 2013 Filed claims Expected Ultimate filed claims Page 24
AM Best’s asbestos and environmental studies have a similar shape P/C Industry Net Asbestos Losses 90 80 70 60 50 40 30 20 10 0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Incurred Losses AM Best Ultimate Based on annual statement data and AM Best Page 25
So what’s in an asbestos range estimate? ► Historical development may have been considered an (un)conceivable extreme scenario or event ► Ranges are based on the best available data at that time ► Survival ratio analyses ► Exposure analyses ► Market share and industry estimates ► Wide range of estimates may result – what is the right range? Page 26
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