Apresentação realizada no 5º Encontro de Resseguro do Rio de Janeiro (abr/2016)

1. Swiss Re Liability Risk Drivers™
Bringing a forward-looking perspective into Liability modelling
Florian Kummer, Hub Head P&C, Latin America, Swiss Re
5 Encontro de Resseguros, Rio de Janeiro, April 5-6

2. • Setting the scene: What’s it all about? / The context
– The nature of Casualty underwriting
– Casualty in LatAm: The risk of change as the dominating underwriting risk
– Property versus Casualty modelling
– Forward looking modelling (FLM): A paradigm shift in casualty underwriting
– Why is it so important?
• Swiss Re Liability Risk Drivers (LRD) model
– Basic model design
– Main components: Modules, liability risk drivers and loss scenarios
• Applications and examples
• In a nutshell: Main benefits for insurers (& insureds)
Agenda
2

3. Accelerating dynamics of risk factors & increasing accumulation exposure
in a long-tail line of business
(considerable time lags between “occurrence”, manifestation and settlement of claims)
What do past loss statistics tell us about future loss costs?
- limited & diminishing predictive value -

Industry’s overreliance on historic losses and past results:
Traditional retrospective or backward looking methods are inadequate for
establishing a set of key economic metrics which are indispensable for
running a sustainable Casualty book.

Setting the scene: What’s it all about? The context
The nature of Casualty underwriting
3
Lack of exposure based steering metrics in Casualty

4. 1. Expected loss costs = pure risk premium
 risk adequate premium
2. Loss distributions = fluctuations around the expected loss
 quantification of tail risk / downside risk
How to cope with these Casualty inherent trends & change dynamics?
How to establish credible metrics?
Forward-looking modelling (FLM):
Scenario based exposure method anticipating and quantifying changes in
external parameters
“We are in the parameter risk underwriting business”
“Transforming our perception of Casualty risk”
Setting the scene: What’s it all about? The context
The nature of Casualty underwriting
4

5. Dynamics of risk factors – context parameters (some examples):
economic
 macroeconomic changes like wage costs, interest rates etc.
social & demographic
 rising claims awareness in middle class societies?
 changing expectations of generation Y?
technological & scientific
 new “digital age” technologies
 new scientific findings on cause – effect relationship of substances
legal & judicial
 evolution of liability regimes (trend towards strict liability?)
 new loss categories? reversal of burden of proof?
 trend in indemnity levels?
 trend towards class actions?
Setting the scene: What’s it all about? The context
The nature of Casualty underwriting
5

6. Dynamics of risk factors – context parameters in LatAm (some examples):
 economic  structural transformation of LatAm economies
 family owned businesses are becoming stock rated companies
 export economies and market openings (more Free Trade Agreements): increasing exports to
highly litigious markets like the US and Europe
 increasing FDI in industrial key sectors
 economic sectors like construction, tourism, agriculture, food industry etc. are gaining weight
 social  structural transformation of LatAm societies
 increasing awareness of rights and obligations in more “middle class societies”
 demographic
 increasing awareness and different expectations of “generation Y”
Setting the scene: What’s it all about? The context
Casualty in LatAm: The risk of change as the dominating underwriting risk
6

7. Dynamics of risk factors – context parameters in LatAm (some examples):
 technological & scientific
 the digital revolution and technological advances in industry and commerce are global trends
 scientific findings have global implications
 legal & judicial
 increasing awareness levels will lead to political pressures and hence to evolving legal and
regulatory frameworks, e.g. bodily injury regimes, environmental regulation etc.
 tendency towards higher indemnity levels
Setting the scene: What’s it all about? The context
Casualty in LatAm: The risk of change as the dominating underwriting risk
7

8. Unlimited Liability Cat (ULC) Limited Liability Cat (LLC) External Event
IllustrationExamples
• Asbestos
• BPA
• EMF
• Deepwater Horizon
• Viareggio train derailment
• Fire in Mont Blanc tunnel
• Law Change
• Life Expectancy
• Inflation
Diverse set of accumulation scenarios = extreme Casualty events:
A Liability Catastrophe causes substantial losses under several insurance policies, and
potentially over multiple years and geographies. Extreme downside risks for insurance
industry.
Setting the scene: What’s it all about? The context
The nature of Casualty underwriting – increasing accumulation risk
8

9. Setting the scene: What’s it all about? The context
Property versus Casualty modelling
Property Cat event = laws of physics
Hazard Vulnerability Values Conditions
Frequency
Loss
Earthquake
Frequency
Loss
Liability
Liability risks / liability events = rules of life
?
?
?
Macro
environment
Social
standards
Legal
system
Conditionsetc.
9

10. Traditional modelling approach based on past loss statistics as an inadequate methodological
approach.
Past losses are not an adequate indicator of future outcomes
Example:
• “Squaring the triangle”: the stability assumption in the Chain Ladder method
Consequences:
• missed opportunities
• unexpected losses
• continuous (or sudden) reserve increases, large immediate payments causing strain on
liquidity and capital levels. Reputational risks and rating implications.
Setting the scene: What’s it all about? The context
Forward looking modelling: A paradigm shift in Casualty underwriting
10
Forward-looking modelling (FLM):
Scenario based exposure method anticipating and quantifying changes in
external parameters

11. Strategic relevance of forward looking modelling in Casualty:
 adequate pricing of casualty risks: tariffs reflect the inherent risk
proactive portfolio steering and management
 risk/reward profile of the book & implications for uw policy
 contribution of individual risks / industries / segments to portfolio profitability
 contribution of individual risks / industries / segments to portfolio loss distribution / extreme
deviations
 comparison of expected loss costs / tariff levels between countries for certain industries
 portfolio impact analysis of societal or legal changes in time
 best practice reinsurance buying
 capital allocation according to inherent (retained) portfolio risk
Important competitive advantage by defining the best policy mix (portfolio
composition/reinsurance buying/capital allocation) according to risk appetite
Setting the scene: What’s it all about? The context
Why is it so important?
11

12. Principles of forward looking modelling:
• Defining all relevant external risk factors (“contextual parameters”) =
anticipating changes and trends
• Defining liability loss scenarios for different industries (including for “extreme”
liability cat events)
• The interaction of these liability risk drivers and liability loss scenarios
anticipate future expected loss costs of liability risks and portfolios by
reflecting on the mechanics and processes that drive them.
• The results are expected loss costs per industry or per portfolio and their
respective distributions
Setting the scene: What’s it all about? The context
Forward looking modelling: A paradigm shift in Casualty underwriting
12

13. Model
Expected
loss
Legal system
What is
covered
and how?
What can go
wrong?
Consequence
How much
does it cost?
Where?
Size of risk?
Aggregation
engine
Exposure
information
Forward-looking model:
 Exposure assessment
based on loss scenarios
 Quantifies effect of
relevant internal and
external risk factors on
losses
 Incorporates validated
expert insights
 Calibrated and validated
against reliable in-house
and external data
 Model’s architecture is
flexible
Exposure Expected
loss
Past
exposure
Loss
experience
World
Model
Comparison
Parameters
Risk-driving
parameters
Loss models
Swiss Re Liability Risk Drivers™ (LRD) Model
Basic model design Patent granted January 2014
Loss
Prevention
Human Factor
Loss
Scenarios
1
2
3
4
5
6
7
13

14. Swiss Re Liability Risk Drivers™ (LRD) Model
Main components: Modules, liability risk drivers and loss scenarios
1 What is the cause? What is the effect? Who is affected?
2 Where does the loss occur? What is the size of the loss?
3 How is the loss mitigated?
4 What does the effect of the loss cost?
5 Will people sue? Which part of the loss is awarded?
6 Which part of the loss is insured / reinsured?
7 Which is the total expected loss and its distribution?
14

15. Model results:
• Expected loss cost (expected average loss) and expected loss distribution
(fluctuation around average) of a single liability risk, an industry segment or
an entire portfolio
• Decomposition of expected loss in contributing factors
• Portfolio risk analysis: Contribution of key risk drivers to overall portfolio
behaviour, for example expected loss per industry segment
• Portfolio impact analysis: Sensitivity analysis for a given risk or portfolio in
case of changes in parameters (liability risk drivers)
• Portfolio benchmarking: Comparison of expected loss costs for a given
industry between countries (jurisdictions)
• Impact analysis of various Casualty cat scenarios on client portfolio
Swiss Re Liability Risk Drivers™ (LRD) Model
Main components: Modules, liability risk drivers and loss scenarios
15

16. Insured in market A
Industry: Food
Expected Loss: USD 50’000
Year 1
Insured in market A
Industry: Food
Expected Loss: USD 65’000
Year 2
Key risk factors
Applications and Examples
Application 1: Anticipation of changes in external parameters
16

17. Insured in market A
Industry: Food
Expected Loss: USD 50’000
Insured in market B
Industry: Food
Expected Loss: USD 20’000
Key risk factors
Applications and Examples
Application 2: Benchmarking of exposures between locations
17

18. 31.06%
100.00%
Expected loss indicator:
Brazil and Germany
A company in country A
pays an amount X of
premium for its
insurance. What would
be the adequate risk
premium for the same
insured in country B?
How can the difference
be explained?
Insured’s
location
Exports Turnover Industry Insurance Covers Claims
Trigger
Insurance Reinsurance
A) Germany
B) Brazil
no €100m SIC 20 -
Food
Public Liability,
Pollution Cover,
Product Liability
Claims
Made
€ 80m xs
50’000
€70m xs
100’000
Applications and Examples
Application 2: Benchmarking of exposures between locations –
Food Industry Brazil / Germany
18

19. 0%
20%
40%
60%
80%
100%
120%
140%
Insured in Brazil Loss Cost (Cost of
Living)
Willingness to
claim
Award
predictability and
fairness
Other liability
laws
Loss Prevention
Standards
Remaining risk
drivers
Insured in
Germany
Expected loss indicator: Food
Brazil
We can explain the difference in expected loss
for similar risks in two different locations by
comparing risk drivers of those locations.
Germany
Applications and Examples
Application 2: Benchmarking of exposures (….) –Food Industry Brazil / Germany
19

20. 0%
20%
40%
60%
80%
100%
120%
140%
Insured in Brazil Loss Cost (Cost of
Living)
Willingness to
claim
Award
predictability and
fairness
Other liability
laws
Loss Prevention
Standards
Remaining risk
drivers
Insured in
Germany
Expected loss indicator: Food
Germany:
higher
willingness
to claim.
2
Influence of
remaining
factors.
5
Germany: claim award
levels more
predictable/less volatile
than in Brazil; no class
action litigation.
3
Germany:
higher loss
prevention
standards
apply.
4
1
Germany:
higher
compensation
levels (costs
of living).
Applications and Examples
Application 2: Benchmarking of exposures (….) –Food Industry Brazil / Germany
20

21. Nano ED XYZ
"buy risk"
"sell risk"
portfolio steering
How do we
get there?
Casualty Accumulation Scenarios
risk appetite
Casualty Accumulation Scenarios
Where do we
want it to be?
Casualty Accumulation Scenarios
exposure
Where does our
portfolio stand?
Applications and Examples
Application 3: Casualty accumulation control for a given portfolio
21

22. Strategic relevance of forward looking modelling in Casualty:
 adequate pricing of casualty risks: tariffs reflect the inherent risk
proactive portfolio steering and management
 risk/reward profile of the book & implications for uw policy
 contribution of individual risks / industries / segments to portfolio profitability
 contribution of individual risks / industries / segments to portfolio loss distribution / extreme
deviations
 comparison of expected loss costs / tariff levels between countries for certain industries
 portfolio impact analysis of societal or legal changes in time
 best practice reinsurance buying
 capital allocation according to inherent (retained) portfolio risk
Important competitive advantage by defining the best policy mix (portfolio
composition/reinsurance buying/capital allocation) according to risk appetite
Setting the scene: What’s it all about? The context
Why is it so important?
22

23. Swiss Re Liability Risk Drivers™
Bringing a forward-looking perspective into Liability modelling
Florian Kummer, Hub Head P&C, Latin America, Swiss Re
5 Encontro de Resseguros, Rio de Janeiro, April 5-6
Muito obrigado!!!

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