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HOW TO USE RISK
QUANTIFICATION TOOLSRob Kingsley, RPX, Vice President
Zoë Rico, Aon, Regional Director
Stephanie Vogel, Aon, Director
Risk Quantification
Pitfalls of Unsupported Decisions
Perceptions Motivations Group Dynamics
Personality Traits Reasoning
Selective Memory
Framing Effects
Selective Attention
Anchoring Effects
Hindsight
Overconfidence
Illusion of Control
Wishful Thinking
Positive Illusions
Premature Harmony
Obedience
Conformity
Anonymity
Attention to Shared Evidence
Psychological Safety
Decision Styles
Comfort Zones
Habitual Frames
Content Selectivity
Nonregressive Predictions
Primary and Recency
Inability to Reason Probabilistically
Attribution Errors
Confirming Evidence
Status Quo
Why Do We Use Models?• Attempt to estimate an unknown to make a better decision
• Simplification of reality to help us understand characteristics of a system
• Useful when it is prohibitive or impossible to observe/test directly
• Important characteristics: Accuracy, Clarity, Flexibility, Efficiency
RealityModel
How Can We Use Models?
Support
Premium
Negotiations
Satisfy
Concerns of
Key
Stakeholders
Reduce Total
Expected
Costs
Manage
Financial
Volatility
Evaluate
Investment
Decisions
Establish
Unpaid
Liability
Estimate
Validate
Operating
Assumptions
I Don’t Have Any Data!• External Data Sources
• Cyber (Advisen, NetDiligence, Ponemon, Verizon, Bloomberg…)
• National Fire Protection Association – Property
• Global Terrorism Database –Terrorism
• Public Financial Data
• US Government sites
• FDA
• YouTube (really!)
• Expert Input
• Scenario workshops to better understand risk
• Expert interviews
• BI Studies
• Engineering Reports
• Broker insights
• Related clients
• AGRC/Benfield/Hewitt colleagues
• Friends/Family (really!)
What Can We Model?Insurable risk models
Portfolio Results
Macro-economic models
What Kind of Model Should We Use?
Model Development
• Expected Value– Mean, average– Most financial forecasts convey the
expected value– Point estimate to represent all potential
outcomes– Does not convey idea of risk
• Variance– Alternative is standard deviation– Describes how far events deviate from
the expected value
• Mean and Variance– Completely describes risk for symmetric
distribution
• Sometimes used for comparing options
Volatility Measures
Model Development
• Risks and opportunities often not symmetric
– Losses bounded below by zero
– Total cost of risk insured risk bounded below by transfer costs
– Rewards bounded below by investment amounts (theoretically unbounded below if additional dollars are continually pumped into a losing investment)
• Skewed distributions
– Not symmetric
– “Fat tails” or “Black Swan”
– The average is no longer at the median (e.g. 50th percentile)
– Same mean and variance can produce different results
Advanced Volatility Measures
0
0.5
1
1.5
2
2.5
-2 0 2 4 6 8
10
12
14
16
18
How to Interpret Information from Your Actuary - Demo
Applying Risk Models and Decision Frameworks
• By leveraging the modelling architecture you can accomplish the following
– Evaluate a wide spectrum of insurance structure options
– Integrate the impact of various hedging strategies
– Develop both tactical (single risk) and strategic (portfolio of risks) diagnostics
– Evaluate non-insurable risks
– Provide an objective view of the risk reward trade-off associated with risk decisions
– Translate the complex modelling process into meaningful financial metrics
Implementing Risk Models and Decision Frameworks
• Focus on the most significant risks
– Largest exposures
– Largest premium spends
• Incorporate relevant exposure drivers
– Commodity prices
– Foreign Exchange
– Demand
• Enhancing Reserve Studies for Low Frequency/High Severity potential risks
• Allow the framework to expand the risk perspective
– Interest rates
– Pension risk
– Operational risk (supply chain)
– Political risk
• Begin to account for all aspects of TCOR that can impact financing decisions
– Risk control investment
– Claims handling
– Asset investment