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ACTUARIAL DATA MANAGEMENT (ADM) IN A HIGH-VOLUME TRANSACTIONAL PROCESSING ENVIRONMENT

Learn about the critical role of Actuarial Data Management (ADM) in optimizing performance and analytical processes for actuarial staff operating in high-volume transactional processing environments, along with key tools and modern roles in end-user computing.

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ACTUARIAL DATA MANAGEMENT (ADM) IN A HIGH-VOLUME TRANSACTIONAL PROCESSING ENVIRONMENT

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  1. ACTUARIAL DATA MANAGEMENT (ADM) IN A HIGH-VOLUME TRANSACTIONAL PROCESSING ENVIRONMENT Joe Strube and Bryant Russell GMAC Insurance, Southfield, Michigan

  2. 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

  3. What Is A High-Volume Transactional Processing Environment (HVTPE)? • Frankly, It’s Arbitrary! • 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

  4. 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 • Non-Programming End Users • Command Level Users • End User Programmers • Functional Support Personnel • End User Computing Support Personnel • DP Programmers

  5. The Square Root of 12,345,678,987,654,321 is 111,111,111

  6. 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 • 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

  7. 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)

  8. Key Roles for ADM in a HVTPE • The Actuary • The Actuarial Technician • Information Technology Dept. (IT)

  9. HVTPE and The Role of the Actuary • HVTPE offers the opportunity to work with detailed, granular data • 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

  10. 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

  11. 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

  12. 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

  13. 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)

  14. 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?

  15. The Role of the Actuarial Technician • Data Facilitator • Building Inspector • Lawyer • Guinea Pig • Data Supplier • Fulcrum • Sculptor • Magician

  16. The Role of IT Management • Manage Infrastructure • Manage Corporate Projects

  17. Data Management Processes • Data Modeling, Metadata, Data Dictionary • Data Extraction, Profiling, Quality • Data Integration, Transformation • Data Loading • Not Data Retrieval, Reporting

  18. There are 336 cavities on a golf ball.

  19. 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

  20. 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

  21. Q & A

  22. In Nawlins . . . It’s mandatory to jazz things up!

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