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
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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
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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

Catastrophe modeling using meteorological,
seismological data
 Financial models

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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

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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:


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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

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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

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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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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