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Actuarial Computing Demands Providing capacity through SaaS

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Actuarial Computing Demands Providing capacity through SaaS

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  1. Page based on Title Slide from Slide Layout palette. Design is 2_Title with graphic. Title text for Title or Divider pages should be 36 pt titles/28 pt for subtitles . PRESENTER box text should be 22pt. DATE text box is not on master and can be deleted. The date should always be 18 pts. Actuarial Computing DemandsProviding capacity through SaaS • Presented by • Van Beach, FSA, MAAA • MG-ALFA Product Manager October, 2010

  2. Page based on Title and Text from Slide Layout palette. Design is 1_Title with photo Subtitles are Part of Title Field, then Modified Manually (see next page) Agenda Milliman and MG-ALFA Evolution of financial modeling Meeting the challenge Benchmark results

  3. Milliman and MG-ALFA • Milliman is a global actuarial consulting firm with over 50 offices worldwide • MG-ALFA is a financial projection system used by actuaries for pricing, risk management, and regulatory reporting • Currently 111 MG-ALFA clients • 193 installations globally • 120 US • Dominate US Market (New & Existing Clients) • Clients in 20 Countries • 2000+ MG-ALFA client users • Milliman consultants are also clients

  4. YE Q1 Q2 Q3 YE Evolution of Financial Modeling • Modeling was an infrequent, “special” process • Annual cash flow testing • Pricing new products • Desktop software enabled actuarial independence and control

  5. YE Q1 Q2 Q3 YE Evolution of Financial Modeling • The models have become more complex • Dependent liability and asset projections • Stochastic analysis (nested stochastic for pricing) • Products and company practices more complicated • More granularity to capture policyholder behavior and other risk characteristics

  6. YE Q1 Q2 Q3 YE Evolution of Financial Modeling • Models are at the core of more functions and analyses • CFT, pricing, principle-based reserving, planning • ALM, EC, C3 Phase 2, C3 Phase 3 • GAAP, IFRS, Solvency II, MCEV, EV • Analysis often requires running several models under consistent bases and assimilating results

  7. YE Q1 Q2 Q3 YE Evolution of Financial Modeling • Models and analyses are required more frequently • Semi-annual economic capital • Quarterly embedded value, planning, ALM • Monthly principle-based reserves • Daily hedging

  8. YE Q1 Q2 Q3 YE Evolution of Financial Modeling • Models are delivering mission-critical information • Reporting windows are tighter • Increasingly viewed as part of the “production” process • More users involved and more consumers of model results

  9. YE Q1 Q2 Q3 YE Evolution of Financial Modeling There is a significant gap between the environment required and the environment that exists to support these requirements

  10. Page based on Title Only from Slide Layout palette. Design is 01_Title with photo. Subtitles are Part of Title Field, then Modified Manually (see next page) Capacity is a critical need Step 1 assess core actuarial projections Step 2 improve capacity Step 3 centralize, control, collaborate Step 2 improve capacity Step 6 automate and integrate Step 5 build macro-model processes Step 4 structure for sustainability

  11. Scalable Cloud Actuarial Infrastructure (SCAI) • Multi-core local desktop computers • Private clouds (i.e., in-house grids) • SaaS (e.g., R Systems) • PaaS (e.g., Azure)

  12. Seriatim policy test • Drivers • Size of the input (in-force) file. • Size of the result file. • The number of servers. • Test parameters • 4 million policies • Large in-force input size is 10* small In-force • With and without reports • 8 cores/server

  13. Runtime benchmarks (Elapsed run time in minutes)

  14. Impact of fixed runtime components (Elapsed run time in minutes)

  15. Stochastic policy test • Test parameters • 2k, 20k, and 200k liability model points • Large in-force input size • With reports • 8 cores/server

  16. Calculation efficiency * 1000 Scenarios were run for each test

  17. Conclusions • R Systems provided a highly scalable computing environment for MG-ALFA • Calculations were very close to linearly scalable • Data movement/processing time was fixed, thereby creating diminishing returns as task size decreased • MG-ALFA is easily reconfigured to change task size • Optimize efficiency or • Optimize runtime

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