Applications of Advanced Science in the New Era of Risk Management Lee Smith, FCAS, MAAA Co-author, Presenter Lilli Segre Tossani, MA, BA Co-author ASSURATECH.
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Transcript Applications of Advanced Science in the New Era of Risk Management Lee Smith, FCAS, MAAA Co-author, Presenter Lilli Segre Tossani, MA, BA Co-author ASSURATECH.
Applications of Advanced Science
in the New Era of Risk Management
Lee Smith, FCAS, MAAA
Co-author, Presenter
Lilli Segre Tossani, MA, BA
Co-author
ASSURATECH
Rapidly Changing Global Risk Landscape
Unimagined new sources of risk
Unimagined risk correlations
Unimagined emergent vulnerabilities
Consolidations within the insurance
industry
Consolidations within insured
industries
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Limitations of Traditional Models
Standard linear statistical models inadequate
to new world of risk
Increasing emergence of fat tails in data patterns
Extreme events cut across products, coverage,
policyholders & financial statement categories
Traditional linear Financial Risk Management models
lack needed flexibility
Stochastic methods cannot accommodate
interactions between multiple marketplace forces
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Models from Outside the Industry
Catastrophe modeling using meteorological,
seismological data
Financial models
Portfolio Theory
Capital Asset Pricing Model
Value at Risk
Questions remain
What risks to measure
How to expand the perspective to include & evaluate
multiple & cascading risks
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Enterprise Models – ERM
Characterizes ERM risk as threats
to organization
Reveals interconnectedness of
operational, event, asset, liability,
information & strategic risk
Cannot accurately value the effects of
multiple & non-linear correlations,
cascading risks or positive feedback
loops
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Enterprise Models – DFA
Projects financial results under a variety of
possible scenarios
Incorporates feedback loops & management
intervention decisions
Combines underwriting & investment
activities to get an overall company view
Traditional linear statistical analysis of
aggregate data + multiple iterations
Slow & ponderous to use
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New Tools for Complex Systems
Need: model simultaneous changes
to multiple variables in complex
environments
Today
all risks are potentially cascading risks
Traditional tools never intended to work with
this expanded risk
Statistical modeling cannot meet the
challenge of today’s non-linear dynamics
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Complexity Science:
The Key to Understanding Complex systems
Technology:
Agent-based
simulation
Bottom-up pattern
recognition
Precision forecasting
Applications:
Risk management
Economic systems
Ecosystems
Investment markets
Santa Fe Institute
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Traditional Systems and Products
Dynamic Financial Analysis (DFA)
Enterprise Risk Management (ERM)
Ranges of values rather than expected values
Better informed decision making
Identifies, categorizes, prioritizes, quantifies all risks
Enables better risk & return assessment
Hedging & diversification strategies to optimize
results
Capital Allocation
Assess profit potential for investment alternatives
Enables allocation by level of risk
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Traditional Systems and Products, cont’d
Rate models
Standard
pricing models
Includes risk-profit load
Catastrophe models
Use
meteorological and seismological data
rather than historical data to estimate costs
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Informatics — The Tools of Complexity
Data Mining
Data Visualization
Extract very complicated patterns, correlations, etc.
from databases
Can mine non-normalized, disparate databases
Visual representation thru charts, graphs,
3D animation, etc.
Allows visual orientation to patterns and trends
Genetic Algorithms
Algorithms mimic Darwinian evolution
Reproduce by random combination to make new
programs
Best problem-solvers survive, continue to evolve
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The Tools of Complexity, cont’d
Neural Networks
Simulate the operation of the human brain
Receive data as input, produce behavior as output
Learn and adapt
Agent-based Simulation
Lower-level agents interact according to simple rules
Behavior of the entire system emerges from the
interactions of the agents
Agents can be non-homogenous, highly interactive
Reveals non-linear, non-intuitive results
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Comparison of Tools
Level of Analysis
Product
Enterprise
Industry
Global
Level of Competency
Cascading Risk
Extreme Events
Non-Linear Results
Disparate Data Mining
Data Visualization
very high
performance
medium
performance
low
performance
not
possible
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Knowledge Capture / Strategic Intelligence
The complexity advantage:
Citibank
uncovered $200M in hidden
credit risk
Proctor and Gamble saved 22% in distribution
expense
Dupont saved $500M annually in
manufacturing expense
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Agent-Based Simulator:
A Scenario Generator
Bench test ideas before committing
resources
Corporate
strategies
Market strategies
Pricing options
Capital allocation
Effects of extreme events
Hidden risks
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Data Mining: Key to Predictive Modeling
Precisely segment markets
Test multi-channel, multi-product
offerings
Optimize marketing budgets
Optimize Customer Relationship
Management (CRM) budgets
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Demand Forecasting
Business systems follow physical laws
well-known in physics
Phase
transitions, hysteresis, etc.
Simple changes in the system can
dramatically impact the bottom line
Uncertainty works to the advantage of
those with knowledge
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InsuranceWorld© Enterprise Simulation
Equity
800
win
Equity ($M)
survive
Lee Smith Co.
700
600
Tom & Dick
500
XYZ Co.
400
Harry & Sons
300
200
Jan-12
Jan-11
Jan-10
Jan-09
Jan-08
Jan-07
Jan-06
Jan-05
0
Jan-04
100
Jan-03
collapse
ABC, Inc.
Quarter
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Complexity Science
Today’s Toolkit for Understanding
Emergent Risk in Complex Systems
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