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Applications of Advanced Science
in the New Era of Risk Management
Lee Smith, FCAS, MAAA
Presenter
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
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Standard linear statistical models inadequate
to new world of risk
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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
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Catastrophe modeling using meteorological,
seismological data
 Financial models
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Portfolio Theory
Capital Asset Pricing Model
Value at Risk
Questions remain
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What risks to measure
 How to expand the perspective to include & evaluate
multiple & cascading risks
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Perspectives on the New Science
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The SFI, formed in 1987, brought together
experts from a variety of areas to address
common interests.
 The technology had emerged from attempts
to solve equations for nonlinear systems of
particles from the A-Bomb chain reaction.
 A fusion of statistical mechanics and
nonlinear dynamics along with advances in
topology, set and group theory, algebraic
geometry, and perturbation theory had
created a new mathematical framework.
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Elements of Complexity Science
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Complexity science is a name which emerged from techniques
developed by thinkers in many venues.
In studying the subatomic world, scientists had been required
to develop computer and math structures to deal with
multidimensional, non-local, fuzzy, virtual, probabilistic entities
which seemed fundamental.
At the classical level, scientists found chaotic and fractal
structures which required interplay between mathematics and
computer science.
Topological features (power laws, fat tails,…) influenced the
collective dynamics of networks.
Data mining and visualization techniques became critical to
unraveling underlying patterns.
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New Tools for Complex Systems
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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
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Technology:
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Agent-based
simulation
Bottom-up pattern
recognition
Precision forecasting
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Applications:
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Risk management
 Economic systems
 Ecosystems
 Investment markets
Santa Fe Institute
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Perspectives from the Physical Sciences
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Information may be as fundamental an
element of the physical universe as matter,
energy, space and time.
 The Standard Model of the Atom,
encompassed in Quantum Field theory,
consists of mathematical “particles” which
exist in a state of probabilistic superposition.
 Our world is a “wedding cake” of layers from
the virtual in the core of the atom through the
subatomic, classical, social, institutional and
cosmic.
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Perspectives from the Physical Sciences,
cont’d

String theory would reconcile macro
relativity with micro quantum, but the
physics is ahead of the math.
 Complexity and chaos theories allow the
physical world to create its own categories,
and develop the math around that.
 The Info Mesa was developed to provide a
forum for reducing reality to data and data to
information.
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Informatics — The Tools of Complexity
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Data Mining
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Data Visualization
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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
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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
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Neural Networks
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Simulate the operation of the human brain
Receive data as input, produce behavior as output
Learn and adapt
Agent-based Simulation
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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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Knowledge Capture / Strategic Intelligence
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The complexity advantage:
 Citibank
uncovered $200M in hidden
credit risk
 Proctor and Gamble saved 22% in distribution
expense
 Dupont saved $500M annually in
manufacturing expense
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Agent-Based Simulator:
A Scenario Generator
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Bench test ideas before committing
resources
 Corporate
strategies
 Market strategies
 Pricing options
 Capital allocation
 Effects of extreme events
 Hidden risks
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Data Mining: Key to Predictive Modeling
Precisely segment markets
 Test multi-channel, multi-product
offerings
 Optimize marketing budgets
 Optimize Customer Relationship
Management (CRM) budgets
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InsuranceWorld© Enterprise Simulation
Equity
800
win
Equity ($M)
survive
Lee Smith Co.
700
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Tom & Dick
500
XYZ Co.
400
Harry & Sons
300
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0
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100
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collapse
ABC, Inc.
Quarter
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Complexity Science
Today’s Toolkit for Understanding
Emergent Risk in Complex Systems
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