CAS Ratemaking Seminar
March 2006:
Data-3: The Actuary and Data
Standards
Data-1: The Actuary and The Data
Manager
1
The Actuary and Data
Standards
Yesterday, Today and
Tomorrow
CAS Ratemaking Seminar
March 2006
2
Agenda
Strategic Data Planning
 Timelines
 The Shifting Focus of Insurance Information
 How Do We Get There?

– Enterprise Data Strategies
– Standards
– Standards and Data Management Best Practices
 10 Guidelines of Data Management

Questions and Commentary
3
Panelists
 Art
Cadorine, ACAS, ISO
 Gary Knoble, AIDM
 Pete Marotta, AIDM, ISO
4
Strategic Data
Planning
5
Data - A Corporate Asset
Data, like all corporate assets, requires
managing to ensure the maximum benefit
is achieved by the organization.
 Well-managed, high-quality data aids
good corporate governance by providing
management with a cohesive and
objective view of an organization’s activity
and promotes data transparency.
 Poorly-managed data can result in faulty
business decisions.

6
Data and Strategic Planning
Data supports corporate decision-making:
 In providing a cohesive and objective view
of corporate activities.
 In viewing the external landscape.
 In predicting the future.
 In developing the corporate strategic plan.
 In identifying process improvements and
other efficiencies.
 In measuring results.
7
PWC Study
“Data is the currency of the new
economy.”
“Companies that manage their data as
a strategic resource and invest in its
quality are already pulling ahead in
terms of reputation and profitability
from those that fail to do so.”
Global Data Management Survey 2001,
PriceWaterhouseCoopers
8
Enterprise Data Strategy: A
Definition


A plan that establishes a long-term direction for
effectively using data resources in support of, and
indivisible from, an organization's goals and
objectives.
An Enterprise data strategy requires both business
and technology input to:
– Facilitate IT planning.
– Support the overall business plan.
– Promote and maintain clearly and consistently
defined data across the corporation.
9
Components of an Enterprise
Data Strategy
Organizational level:
 Data Stewardship
– Senior level oversight of corporate data.
– From an enterprise-wide perspective.
 Data Architecture – What to Run, Where to
Run, How to Run – Software and Hardware:
– Ownership: Customer and Data
– Data Location
– Software v. Service
– Product Definition
 Data and Process Models
10
Components of an Enterprise
Data Strategy
Data level :
 Data Element Management
– Data Definition and Attributes
– Code Value and Data Set Management
– Data Mapping Management
 Data Quality
 Data Standards
– Business and Efficiency Driven
– Internal and External
 Data Privacy and Security
– Compliance with Privacy Polices and
Regulations
– Data from Reputable Sources
– Data Security
11
Strategic Data Planning
 Strategic
Data Planning is primarily a
business, not an IT function.
 IT critical to any enterprise data
strategy.
12
Enterprise Data Strategy and IT:
Architecture Supports Business Strategy
A set of guiding
principles that
define why and
what we do
Data
Application
Infrastructure
Business Strategy
A set of guiding
principles that
define how we
do what we do
IT Architecture
13
Results of a Successful
Enterprise Data Strategy
Provide a process and a set of tools to
facilitate Business and IT planning and
decision-making
 Maintain a common and consistent view
of data that is shared company wide
 Facilitate alignment and traceability of
significant IT investments to their
respective business drivers

14
Business Results of Enterprise
Data
 Ease
of doing business
 Speed to market
 Facilitate R&D
 Customer Service
 Compliance
15
Timelines
16
The Past
Regulators/Business
(underwriters, actuaries, etc.)
Coverage Forms
(changes in forms and coverages)
Data Standards
17
Today
Technology
Financial
(Internet, XML,
(SOX, GLB, HIPAA, etc.)
Black Boxes, RFIDs)
3rd Parties
(Credit, DMV,
etc.)
Data Standards
18
Tomorrow
Business Needs: Business, regulatory, technology, etc.
(Profitability, Loss Control, Consumer Protection, Solvency,
Privacy, Confidentiality, etc.)
Data Needs
Data Standards
19
The Shifting Focus of
Insurance Information
20
Regulation



From Annual Statement to Market Conduct
Annual Statements to NAIC Databases
– Financial Data Repository (FDR)
– National Insurance Producer Registry (NIPR)
– Fingerprint Repository
– On-Line Fraud Reporting System (OFRS)
– Uninsured Motorist Identification Database
From financial data used to monitor solvency
to financial, statistical data and analytics used
to monitor solvency
From US driven regulations to EU and
internationally driven regulations
21
Pricing



From traditional underwriting and pricing - using
traditional data sources (risk data, industry
statistics) to predictive modeling and analytics using non-traditional data sources
(demographics, GIS, 3rd party data, noninsurance data, non-verifiable data sources, etc.)
From a stable risk control and claims
environment to a dynamic environment of new
hazards - mold, terrorism, computer viruses,
cyber terrorism, etc.
From risk-specific risk management to enterprise
risk management
22
Data
From a data quality focus on validity,
timeliness and accuracy to a data quality
focus on transparency, completeness and
accuracy
 From data available on a periodic basis to
data available real-time
 From statistical plans and edit packages to
data dictionaries, schema and
implementation guides
 From sharing data for the common good
to protecting data for the common good

23
Technology
 From
centralized highly controlled
technologies to ASPs, the, Internet,
XML, LANs, PCs, etc.
 From IT as an business enabler to IT
as a business driver
 From mainframes to LANS and high
powered PCs
24
How Do We Get There?
25
How do we get there?
 Enterprise
Data Strategies
– Assemble the right team
– Business Needs – internal and external,
current and future
– Technology – current and future
– New Products
 New
Processes
 Standards
 Best Practices
26
Data Users, Data Definers
& Data Enablers










Business Units (Underwriters)
Information Technology
Finance and Accounting
Actuaries
Claims
Government Affairs
Sales and Marketing
Research
Data Management
Data Element Management
27
New Processes: The Goal – Single Entry
A
Real Time
data entry
B
Download
Solution
Carrier
Provider/Vendor
B – Carrier processes data,
syncronizes with agency data base
through download
Producer/
agent/
Broker
A – Form/Msg from Producer
(agent/broker) to Carrier
Producer either waits for download,
or does data entry to process binder,
ID cards, certs.
Re-use of
data
“enabler”
Service
Provide
r
Reinsurer
C
D
D – Data may continue along the process
to be used by Reinsurers, etc.
C – Messages from Carrier to Service
Providers (CLUE, MVR)
28
Straight Through Processing
(STP)
The
use of common, industry standard
data elements, throughout all
interactions of all parties, in all
insurance transactions or processes.
STP allows data to flow effortlessly
through the industry without
redefinition, mappings or translations.
29
STP Vision
Provides a common set of definitions
– Data definitions
– Not of every transaction or message
 Allows consistent industry solutions
– Vendor provided software solutions
– Internally developed applications
 Facilitates exchange of information
 Eliminates mappings and translations
 Minimizes friction

30
STP Value
 Improves
data quality, utility
– better benchmarking
 Lessens
data translations, eliminates
return transactions for clarification
 Reduces friction in insurance
processes
 Allows companies to differentiate on
value added
 Facilitates “plug and play” solutions
31
STP Benefits
 Improved
Customer Relationship
– Less Time Processing
 Ease
of Doing Business
 Retention and Growth
 Profitability
32
Standards
33
What are Standards?
Definition: Standard (n.)
“Anything recognized as
correct by common consent,
by approved custom, or by
those most competent to
decide; a model; a criterion.”
-- Webster’s New Universal Dictionary
34
Types of Standards
 Business
Models
– Identify All the Major Processes and
Relationships
 Common
Insurance Terminology
 Coverage and Forms
 Process Standards
– Application Forms, Report of Injury or
Claim, Licensing, etc.
35
Types of Standards (Continued)
 Other
– Solvency Standards
– Financial Information Exchange
Standards
– Market Conduct Information Standards
– Ratemaking Standards
– Operating Data Standards
– Data Exchange Standards
– Data Quality Standards
36
ACORD Standards
Doing Things Once Has Many Benefits
 Data names
 Data definitions
 Paper or electronic operational forms
 Machine readable formats
 Business Process Models
 Code list definitions
 Data transmission standards

37
Data Collection Organization
Standards
 Policy
Forms and Coverages
 Rate Making Standards
 Data Reporting Standards
 Data Quality Standards
 Data Element Definitions
 Code List Definitions
38
Business Process
A business process is a collection of
related structural activities that
produce something of value to the
organization, its stake holders or its
customers.
It is, for example, the process
through which an organization
realizes its services to its customers.
39
Business Rules
Business rules describe the
operations, definitions and
constraints that apply to an
organization in achieving its goals.
For example a business rule might
state that no credit check is to be
performed on return customers.
40
Need for Industry Collaboration
Submission
Insurance
Carriers
Broker/Insurer
Regulatory
Compliance
Ins/Reinsurer
Claims
Regulatory
Authorities
Reinsurer
Claims
Management
Applications
Auditing
Insurance
Agency
Premium
transactions
Payment
transactions
Service
Providers
Agent/
Producer
41
Benefits of Industry Data Standards
Submission
Insurance
Carriers
Regulatory
Compliance
Broker/Insurer
Ins/Reinsurer
Reinsurer
Claims
Management
Applications
Insurance
Agency
Claims
Regulatory
Authorities
STANDARDS
&
IMPLEMENTATION
Premium
transactions
Payment
transactions
Auditing
Service
Providers
Agent/
Producer
42
Standards and Data
Management Best
Practices
43
10 Guidelines of Data
Management
1.
2.
Data must be fit for the intended
business use.
Data should be obtained from the
authoritative and appropriate
source.
44
10 Guidelines of Data
Management
3.
4.
Data should be input only once
and edited, validated, and
corrected at the point of entry.
Data should be captured and
stored as informational values, not
codes.
45
10 Guidelines of Data
Management
5.
6.
Data should have a different steward
responsible for defining the data,
identifying and enforcing the business
rules, reconciling the data to the
benchmark source, assuring
completeness, and managing data quality.
Common data elements must have a
single documented definition and be
supported by documented business rules.
46
10 Guidelines of Data
Management
7.
8.
Metadata must be readily available to all
authorized users of the data
Industry standards must be consulted
and reviewed before a new data element
is created
47
10 Guidelines of Data
Management
9.
10.
Data must be readily available to all
appropriate users and protected against
inappropriate access and use
Data users will use agreed upon common
tools and platforms throughout the
enterprise
48
Questions and
Commentary
49
The Actuary and The
Data Manager
Custodians of Enterprise Data
Assets
CAS Ratemaking Seminar
March 2006
50
Agenda
Data Management Best Practices
 10 Guidelines of Data Management




Timelines
The Shifting Focus of Insurance Information
Information Quality and Assurance
– Data Quality
– Data Transparency
– ASOP #23




Regulatory Requirements and the Role of Data
IDMA Data Management Value Propositions
Questions and Commentary
Organizations That Can Help
51
Panelists
 Art
Cadorine, ACAS, ISO
 Bruce Tollefson, MN WC Rating
Bureau
 Christine Siekierski, WI Comp. Rating
Bureau
 Pete Marotta, AIDM, ISO
52
Data Management
Best Practices
53
Data Management Best Practices
 Data
Stewardship – establish a
corporate data steward
 Data and Data Quality Standards –
foster the development and adoption
of data and data quality standards
 Organizational Issues – structure
organization to promote good data
management and data quality
54
Data Management Best Practices
 Operations
and Processes –
establish processes to maximize
data quality and utility
 Data Element Development and
Specification – design and
maintain data, systems and
reporting mechanisms in a manner
that promotes good data
management and data quality
55
10 Guidelines of Data
Management
56
10 Guidelines of Data
Management
1.
2.
Data must be fit for the intended
business use.
Data should be obtained from the
authoritative and appropriate
source.
57
10 Guidelines of Data
Management
3.
4.
Data should be input only once
and edited, validated, and
corrected at the point of entry.
Data should be captured and
stored as informational values, not
codes.
58
10 Guidelines of Data
Management
5.
6.
Data should have a different steward
responsible for defining the data,
identifying and enforcing the business
rules, reconciling the data to the
benchmark source, assuring
completeness, and managing data quality.
Common data elements must have a
single documented definition and be
supported by documented business rules.
59
10 Guidelines of Data
Management
7.
8.
Metadata must be readily available to all
authorized users of the data
Industry standards must be consulted
and reviewed before a new data element
is created
60
10 Guidelines of Data
Management
9.
10.
Data must be readily available to all
appropriate users and protected against
inappropriate access and use
Data users will use agreed upon common
tools and platforms throughout the
enterprise
61
Timelines
62
The Past
Regulators/Business
(underwriters, actuaries, etc.)
Coverage Forms
(changes in forms and coverages)
Data Standards
63
Today
Technology
Financial
(Internet, XML,
(SOX, GLB, HIPAA, etc.)
Black Boxes, RFIDs)
3rd Parties
(Credit, DMV,
etc.)
Data Standards
64
Tomorrow
Business Needs: Business, regulatory, technology, etc.
(Profitability, Loss Control, Consumer Protection, Solvency,
Privacy, Confidentiality, etc.)
Data Needs
Data Standards
65
The Shifting Focus of
Insurance Information
66
Regulation



From Annual Statement to Market Conduct
Annual Statements to NAIC Databases
– Financial Data Repository (FDR)
– National Insurance Producer Registry (NIPR)
– Fingerprint Repository
– On-Line Fraud Reporting System (OFRS)
– Uninsured Motorist Identification Database
From financial data used to monitor solvency
to financial, statistical data and analytics used
to monitor solvency
From US driven regulations to EU and
internationally driven regulations
67
Pricing



From traditional underwriting and pricing - using
traditional data sources (risk data, industry
statistics) to predictive modeling and analytics using non-traditional data sources
(demographics, GIS, 3rd party data, noninsurance data, non-verifiable data sources, etc.)
From a stable risk control and claims
environment to a dynamic environment of new
hazards - mold, terrorism, computer viruses,
cyber terrorism, etc.
From risk-specific risk management to enterprise
risk management
68
Data
From a data quality focus on validity,
timeliness and accuracy to a data quality
focus on transparency, completeness and
accuracy
 From data available on a periodic basis to
data available real-time
 From statistical plans and edit packages to
data dictionaries, schema and
implementation guides
 From sharing data for the common good
to protecting data for the common good

69
Technology
 From
centralized highly controlled
technologies to ASPs, the, Internet,
XML, LANs, PCs, etc.
 From IT as an business enabler to IT
as a business driver
 From mainframes to LANS and high
powered PCs
70
Information Quality
and Assurance
71
Data Quality
Data Quality is defined as the
process for ensuring that data are
fit for the use intended by
measuring and improving its
key characteristics.
72
Managing Data & Data Quality:
Guiding Principles
 Data
is a corporate asset
 Data should be fit for the use
intended
 Data should flow from underlying
business processes
 Data quality should be managed as
close to the source as possible
 Best Practices are ever evolving
73
Data Quality: Key Characteristics
Fit for its intended use
 Accuracy
 Validity
 Timeliness and Other Timing Criteria
 Completeness or Entirety
 Reasonability
 Absence of Redundancy
 Accessibility, Availability and
Cohesiveness
 Privacy
74
Data Transparency: Key Characteristics








Data defined and documented
Utility across time and source
Supports internal controls.
Clear, standardized, comparable information
Facilitates assessment of the health of the
systems using the data
Promotes better controls
Improves operational and financial performance
Documents data elements, data element
transformations and processes
75
ASOP #23: Data Quality
 Purpose
is to give guidance in:
– Selecting data
– Reviewing data for appropriateness,
reasonableness, and
comprehensiveness
– Making appropriate disclosures
 Does
not recommend that actuaries
audit data
76
ASAP #23: Data Quality
Considerations in Selection of Data
 Appropriateness
for intended
purpose
 Reasonableness, comprehensiveness,
and consistency
 Limitations of or modifications to
data
 Cost and feasibility of alternatives
 Sampling methods
77
ASOP #23: Data Quality
Definition of Data
 Numerical,
census, or class
information
 Not actuarial assumptions
 Not computer software
 Definition of comprehensive
 Definition of appropriate
78
ASAP #23: Data Quality
Other Considerations
 Imperfect
Data
 Reliance on Others
 Documentation/Disclosure
79
Regulatory
Requirements and the
Role of Data
80
Why Regulation?

It’s all about consumer protection
– Solvency
 Ensuring that companies are financially
sound and able to pay claims
– Market Conduct
 Point of sale and service
 Ensuring that the agent is licensed and
appointed, the customer understands the
coverage, claims are handled effectively (i.e.
injured workers are paid on a timely basis)
– Rate Adequacy
81
The Impact of Standards on the
US Regulatory Landscape

US Office of Management & Budget
Circular A-119
– “[Government] agencies should
recognize the positive contribution of
standards development and related
activities. When properly conducted,
standards development can increase
productivity and efficiency in
Government and industry, expand
opportunities for international trade,
conserve resources...”
82
The Impact of Standards on the
US Regulatory Landscape
 Government
should utilize standards
built by the industry and
implemented within company
operations
– Cuts expenses
– Ensures STP and quality
83
Industry, State and Federal
Requirements
State
Industry
DOIs
Rating Bureaus
WC Commissions
Stat Agencies
Residual Market Plans
Insurance
Company
DMVs
DOTs
Data Collection
Data Storage
Data Sharing
Federal
SEC
Treasury
Homeland Security
HHS
84
Regulatory Issues & Data
Reporting Requirements
– Financial
– DMV
– Workers Compensation
– Statistical
 Market Conduct
 Operations
– Electronic Applications
UETA
eSIGN
Privacy (HIPAA, GLB)

85
Current Successes in Standardizing
Data for Regulatory Purposes



Workers Compensation Insurance
– Boards and bureaus (statistical reporting)
– State WC Commissions (proof of coverage and
monitoring claims)
Producer licensing and appointments
– Producer to carrier information needs
– State issues such as National Producer Number
State application compliance and filings
– Interstate Compact
86
Accountability, Quality,
Transparency Regulations



Sarbanes Oxley
– US law ensuring accuracy of financial data with
accountability of company executives
Solvency II
– EU proposal similar to SOX addressing financial
reporting and public disclosure
Reinsurance Transparency
– International Association of Insurance
Supervisors working group to explore solvency
of reinsurers worldwide. Differences in data
definitions are presenting a challenge
87
“SOX” and the Data Manager
The importance and visibility of Data
Management among senior executives and
regulators has increased.
 The importance of Data as an important
corporate resources has increased.
 The contribution of Data Management to
proper data and process control is more
widely recognized.
 The demand for data quality has
increased.

88
IDMA Data Management
Value Propositions
89
Data Management Value



Product Development and Revenue
Generation: Maintains data management
processes and tools that promote speedto-market of new products and services
Enhances customer acquisition, retention,
service and satisfaction through good
quality customer data
Maintains the data management processes
and tools that support the pricing of
insurance products
90
Data Management Value
 Provides
an enterprise
communication channel for new
products, services, programs and
technologies that allows all facets of
the organization to evaluate the
impact of these changes
 Specifies data needed to support new
products and ensures that these data
are assessable in a timely manner
91
Data Management Value

Efficiency and Utility
– Reduces the cost of data collection, storage,
and dispersal
– Manages data content and definition across
the organization
– Advocates industry and enterprise data
standards which insure consistent definitions
and values for enterprise data elements
– Ensures accurate booking of premium and
loss transactions
– Ensures the quality of the enterprise data
– Promotes the interoperability of data and
databases
92
Data Management Value


Strategic Planning
– Participates in the development of an
enterprise data vision and strategy
– Monitors external activities and reporting on
potential impact on enterprise
Compliance
– Protects the privacy and confidentiality of the
enterprise data
– Ensures compliance with data reporting laws
and regulations,
– Represents the organization to regulators,
workers’ compensation administrators,
advisory organizations, research
organizations, standards organizations and
other industry groups
93
Questions and
Commentary
94