INDIAN SCIENCE CONGRESS
Mumbai 2015
Actuarial Science Symposium
G. P. Patil
Penn State University, University Park,
PA USA
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INSURANCE ENTERPRISE RISK MANAGEMENT
CONTROL CYCLE :
CERTAIN STATISICAL ISSUES AND APPROACHES
Multi-Indicator Systems for Ranking,
Prioritization, Detection, and Selection with
Multiple Risk Measures
• Risk Monitoring, Data Collection, Selection Bias,
and Weighted Distributions
• Personal Involvements with Actuarial-type Areas
Insurance ERM Control Cycle
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A PUBLIC POLICY PRACTICE NOTE
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EXPOSURE DRAFT
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• Insurance Enterprise Risk Management
• Practices
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March 2013
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Developed by the ERM Committee
of the American Academy of Actuaries
Stochastic Modeling
• Involves estimating statistical distributions
of potential outcomes using random variables for
one or more inputs over time.
. Could include ESG simulations of potential
outcomes of the economies and financial markets.
. The distributions of potential outcomes and
extreme losses indicated by stochastic models often
form the basis for computing key risk metrics/
measures of the organization.
Data requirements and risk model
selections are inter-related
• The choice of risk models will affect data
requirements. Modeling complexities and
options involve data element choices.
Actuaries need to understand the impact of
the values of the data elements on the key risk
metrics used by the organization.The impacts
are implicit also on risk monitoring and risk
mitigation.
Offender Actuarial Risk Assessment
• Actuarial risk assessment focusses on both
static/ unchangeable and dynamic
factors that influence recidivism ( all types of
criminal offences ).
. Some notable examples of actuarial scales are:
Violence Risk Appraisal Guide ( VRAG )
Statistical Information on Recidivism Scale ( SIR )
Sex Offender Need Assessment Rating ( SONAR )
Multiple Actuarial Measures
• Different actuarial risk measures produce
different risk rankings for sexual offenders
• Five actuarial risk instruments commonly used
with adult sex offenders:
• RRASOR, Static99, VRAG, SORAG, MnSOST-R.
• Discrepancies in percentile ranks; Rank ranges
vary inversely to correlations between risk
measures
• Guidance to clinicians in resolving discrepancies
between instruments based on underlying
factors, such as, antisocial behavior, etc.
NSF Digital Government surveillance geoinformatics
project, federal agency partnership and national
applications for digital governance.
Homeland
Security
Disaster
Management
Public
Health
Ecosystem
Health
Federal Agency
Partnership
Other Case
Studies
CDC DOD EPA
NASA NIH
NOAA USFS USGS
Survellance Geoinformatics of
Hotspot Detection, Prioritization and
Early Warning
Statistical Processing: Hotspot Detection, Prioritization, etc.
NSF Digital Government Project #0307010
Arbitrary Data Model, Data Format, Data Access
PI: G. P. Patil
Application Specific De Facto Data/Information Standard
National and International
Applications
[email protected]
Standard or De Facto Data Model, Data Format, Data Access
Data Sharing, Interoperable Middleware
Some linear extensions
Poset
Agency Databases
Thematic Databases
(Hasse Diagram)
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Poset
Other Databases
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Linear extension decision tree
Some linear extensions
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(Hasse Diagram)
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Cellular Surface
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Websites:
http://www.stat.psu.edu/~gpp/
http://www.stat.psu.edu/hotspots/
http://www.stat.psu.edu/%7Egpp/DGOnlineNews2006.mht
Masks, filters
Indicators, weights
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Geoinformatic Surveillance System
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Geoinformatic spatio-temporal
data from a variety of data
products and data sources with
agencies, academia, and industry
• Biosurveillance
• Homeland Security
• Carbon Management
• Invasive Species
Linear
decision
tree Policy
• Coastalextension
Management
• Poverty
• Community Infrastructure • Public Health
• Crop Surveillance
• Public Health and
• Disaster Management
Environment
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• Disease Surveillance
• Robotic Networks
• Ecosystem Health
• Sensor Networks
• Social
Networks
c • Environmental
b Justice
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• Environmental
• Syndromic Surveillance
Management
• Tsunami Inundation
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Policy
b• Environmental
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c • Urban dCrime a
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• Water Management
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Spatially
distributed
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response
variables
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ePrioritization
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Hotspot
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Decision
support
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systems
Masks, filters
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Upper endpoints
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Rank Intervals
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Midpoints
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Lower endpoints
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Midpoint
Figure 14. Rank-intervals for all 106 countries. The intervals
(countries) are labeled by their midpoints as shown along the horizontal
axis. For each interval, the lower endpoint and the upper endpoint are
shown vertically. The length of each interval corresponds to the
ambiguity inherent in attempting to rank that country among all 106
countries.
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Actuarial Data Collection, Selection
Bias, and Weighted Distributions
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Sampling individuals at random in a survey, but recording the life lengths for record.
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For X , exponential,with mean 1,
X* has mean 2, double that of X,
as a result of size bias in selection.
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Patil and Rao
Patil, Rao, and Zelen
Patil, and Taillie
Patil, Rao, and Ratnaparkhi
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• recorded x is not an observation on X, but on the rv
• X , say, having a pdf
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where ω is the normalizing factor obtained to make
the total probability equal to unity by choosing ω D
E[wX, ˇ]. The rv X is called the weighted version
of X, and its distribution in relation to that of X is
called the weighted distribution with weight function
w.
w