2005 Ratemaking
Seminar
ACTUARIAL DATA MANAGEMENT (ADM)
IN A HIGH-VOLUME
TRANSACTIONAL PROCESSING
ENVIRONMENT
Joe Strube and Bryant Russell
GMAC Insurance, Southfield, Michigan
2005 Ratemaking
Seminar
 Goal of the ADM Function
 Equip the Actuarial Staff with the data resources
necessary to excel in the performance of their
functions
 Not simply “get the actuaries data”
• Add value to their analytical processes
• C.A.T. Criteria
– Complete (Collect, Consolidate, Derive)
– Accurate (Clean dirty/distorted data)
– Timely (Prioritize data resource deliveries)
• Possibly assume responsibility for next stage
2005 Ratemaking
Seminar
 What Is A High-Volume Transactional Processing
Environment (HVTPE)?
 Frankly, It’s Arbitrary!
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Online Transaction Processing System (OLTP)
Mainframe/Midrange Server Extract Files
Operational Data Store (ODS)
Data Warehouse (DW)
Data Mart (DM)
Desktop DB
 Consider your most granular actuarial data resource
 Are 1 million or more transactions added per month?
 If YES, you’re operating in an HVTPE
2005 Ratemaking
Seminar
 ADM, An Outgrowth of End User Computing
 Rockart & Flannery Research Study (1983)
• Sloan School of Management at MIT
• Interviewed 250 People
• 3 Fortune 50 Manufacturers
• 2 Major Insurance Companies
• 3 Sizable Canadian Companies
• Identified Six Types of End Users
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Non-Programming End Users
Command Level Users
End User Programmers
Functional Support Personnel
End User Computing Support Personnel
DP Programmers
2005 Ratemaking
Seminar
The Square Root
of
12,345,678,987,654,321
is
111,111,111
2005 Ratemaking
Seminar
 As Technology Evolves, So Do Deliverables
 Mainframe to Minicomputer to Microcomputer (PC)
 Complex mainframe programs
• Originally produced reports
• Historical data files
• Data downloads
 Data Management Technology Branches Out
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Data Warehousing
Data Marts
Online Analytical Processing (OLAP)
Extraction-Transformation-Loading Software (ETL)
Meta Data Repositories
Decision Support Systems
Data Profiling/Cleaning/Integration Software
Data Mining
2005 Ratemaking
Seminar
 Modern Roles in End User Computing
 Non-Programming End Users
(Business Manager, Process Modeler, Trainer)
 Command Level Users
(HR Rep, Accountant, Claim Analyst, Market Analyst)
 End User Programmers
(Actuary, Financial Analyst, Strategic Planner)
 Functional Support Personnel
(Data Manager/Administrator, Actuarial Technician)
 End User Computing Support Personnel
(Help Desk, User Hotline, DSS Analyst, DW Support Team)
 DP Programmers (a.k.a. Systems Analyst)
(Internal/Outside Contractor, Technical Consultant)
2005 Ratemaking
Seminar
 Key Roles for ADM in a HVTPE
 The Actuary
 The Actuarial Technician
 Information Technology Dept. (IT)
2005 Ratemaking
Seminar
 HVTPE and The Role of the Actuary
 HVTPE offers the opportunity to work with detailed,
granular data
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Classification Analyses
GLM Analyses
Loss Distributions
Data Mining
 Comparison of data sources across functional areas
becomes possible
• Policy Year vs. Accident Year vs. Calendar/Accident Year
“slices” can be reconciled more easily
• Common data source overcomes the “my data-your data”
syndrome
2005 Ratemaking
Seminar
 The Role of the Actuary
 HVTPE is inherently multidimensional
• Transactional data is very granular, detailed
• Multiple views can be aggregated from common source
(transactional data)
• Very useful for examining interactions between factors
 As a “source”, HVTPE allows actuarial data
repositories to be created, stored, and maintained over
time
• Actuary can select data based on actuarial value
• Less reliance on non-actuarial reports
2005 Ratemaking
Seminar
 Q: Why not let the actuary do it all?
 Many actuaries well-versed with database and
analytical software
 But data from HVTPE is just the first step
• Data extraction is input to analyses
• Actuarial work typically requires more than just a
summary report of historical experience
 Data extraction process can overwhelm traditional
desktop tools
• Over 1 million transactions per month
• Even data storage, can overwhelm desktop resources
available to actuary
2005 Ratemaking
Seminar
 Q: Why not let others (ADM) do it all?
 Data specialization enables more robust process of
gathering, storing, reporting data
• Data skills are specialized
• Software and hardware can be “industrial strength”
• Monitoring, balancing, aggregating are important, but
non-actuarial tasks
 HVTPE is not a static environment
• Changes to data definitions
• Addition of new data elements
• Addition of new data sources
2005 Ratemaking
Seminar
 Q: Why not let others (ADM) do it all?
 Value of actuarial data elements may be seen as
secondary to other functional areas
• Required level of detail for actuarial analysis is different
• Historical retention periods are different
• Specific data elements may be uniquely valuable to the
actuary – other areas would not gather/maintain these.
 Actuarial data needs are dynamic
• Summary level varies by type of actuarial analysis
• Variables included can range from few to many
• Not realistic to try & build all possible aggregations ahead
of time (OLAP tools notwithstanding)
2005 Ratemaking
Seminar
 The Role of the Actuary
 Identify the value of actuarial data
• Critical Data Elements
• Actuarially Valuable Data Elements
• Nonessential Data Elements (for actuarial analysis)
 Determine required level of detail
• Granularity of data (e.g. at transactional level)
• Historical time periods and retention periods
• Definitions of derived data (e.g. books of business,
classes, etc.)
 Support the Value Proposition of Data Dictionary
• What does the data mean? What are meaningful values?
• Have definitions, coding, accuracy, completeness changed over
time?
2005 Ratemaking
Seminar
 The Role of the Actuarial Technician
 Data Facilitator
• Building Inspector
• Lawyer
• Guinea Pig
 Data Supplier
• Fulcrum
• Sculptor
• Magician
2005 Ratemaking
Seminar
 The Role of IT Management
 Manage Infrastructure
 Manage Corporate Projects
2005 Ratemaking
Seminar
 Data Management Processes
 Data Modeling, Metadata, Data Dictionary
 Data Extraction, Profiling, Quality
 Data Integration, Transformation
 Data Loading
 Not Data Retrieval, Reporting
2005 Ratemaking
Seminar
There are
336 cavities
on a golf ball.
2005 Ratemaking
Seminar
 GMAC Insurance Case Study
 Vehicle Service Contracts
• Multiple period contracts – under 12 months to over 84
months
• Multiple countries and business partners
• Multiple data sources
• Over 1 million transactions per month
2005 Ratemaking
Seminar
 GMAC Insurance Case Study
 Vehicle Service Contracts
• Multiple period contracts – under 12 months to over 84
months
• Multiple countries and business partners
• Multiple data sources
• Over 1 million transactions per month
2005 Ratemaking
Seminar
Q&A
2005 Ratemaking
Seminar
In Nawlins . . .
It’s mandatory to
jazz things up!