Blowing through the range 2014 CLRS presentation September 15, 2014 - - PowerPoint PPT Presentation

Blowing through the range 2014 CLRS presentation

September 15, 2014 Christopher Andersen, FCAS, MAAA Ron Fowler, FCAS, MAAA

Page 2

Agenda

► Actuarial ranges and their context ► Communication of the actuarial range ► When things go wrong – Case studies

► Financial guarantee ► Catastrophes ► Asbestos

Page 3

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 4

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
• r 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 5

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 6

When things go wrong – Case studies

► 2008 financial crises

► Financial guarantee insurance

► Unusual catastrophes

► Hurricane Katrina ► Thailand flooding ► New Zealand earthquake

► Asbestos

Page 7

The 2008 financial crises was more sudden than previous depressions in the financial markets

• 40.0%
• 30.0%
• 20.0%
• 10.0%

0.0% 10.0% 20.0% 30.0%

DJIA – Year over year percentage change

Prepared by EY

Page 8

Industry loss ratio spiked to 200% to 300% and have still not recovered to pre-financial crisis levels

0.0% 100.0% 200.0% 300.0% 400.0% 500.0% 600.0% 700.0% 800.0% 900.0% 1000.0%

Loss ratios for Industry and Top Financial Guarantee and Mortgage-Back Securities writers

P&C Industry Company 1 Company 2 Company 3 Company 4

Based on SNL data; Prepared by EY

Page 9

Unprecedented financial market performance underlined inadequate reserve amounts in financial guarantee and mortgage guarantee products

• 50.0%
• 25.0%

0.0% 25.0% 50.0% 75.0% 100.0% 125.0%

1 year reserve development / Prior year reserves

Commercial Auto Workers Compensation Medical Professional Fidelity and surety Financial Guarantee and Mortgage Gaurantee

Based on SNL data; Prepared by EY

Page 10

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 11

2006 vintage curves indicated stability

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 2 8 14 20 26 32 38 44 50 56 62 68 74 80 86 92 98 104 110 116 122 128 Severity Loan Age 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Page 12

Post 2008 financial crisis the historical development was no longer relevant

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 2 8 14 20 26 32 38 44 50 56 62 68 74 80 86 92 98 104 110 116 122 128 Severity Loan Age 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Page 13

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% E Used 2005 probabilities for all

• lder vintages

Increased by 7.5% +57% F Used 2007 probabilities for all vintages No change +118%

Page 14

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 15

When things go wrong – Case studies

► 2008 financial crises

► Financial guarantee insurance

► Unusual catastrophes

► Hurricane Katrina ► Thailand flooding ► New Zealand earthquake

► Asbestos

Page 16

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 17

The Thailand Flooding claims have been harder to identify and investigate due to the lingering water in warehouses

Prepared by EY; based on RAA data. 1 period is equal to 3 months

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0% 1 3 5 7 9 11 13 15 17 19 % Case incurred to ultimate Periods Thailand Floods Katrina Rita Wilma NZ2 EQ Japan EQ WTC

►

Significantly lower after 6 periods

►

Time-element coverage is causing uncertainty

Page 18

Historical earthquake % of case incurred to ultimate development

Prepared by EY; based on RAA data. 1 period is equal to 3 months

0.0% 20.0% 40.0% 60.0% 80.0% 100.0% 120.0% 1 2 3 4 5 6 7 8 9 1011121314151617181920 % Case incurred to ultimate Periods NZ EQ2 Chile Japan Loma Prieta Northridge NZ EQ1 NZ EQ3

►

The case incurred amounts continue to rise through 9 report periods

Page 19

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 20

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

Liquefaction

Page 23

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 24

A history of predictions and re-predictions

► The poor track record of estimating asbestos

100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000 1,000,000 1988 1993 1997 2005 2013

Manville Trust – Asbestos Claims filing

Filed claims Expected Ultimate filed claims

Page 25

AM Best’s asbestos and environmental studies have a similar shape

10 20 30 40 50 60 70 80 90 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

P/C Industry Net Asbestos Losses

Incurred Losses AM Best Ultimate

Based on annual statement data and AM Best

Page 26

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?