Actuarial Computing Demands Providing capacity through SaaS Presented by Van Beach, FSA, MAAA MG-ALFA Product Manager October, 2010 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.

2 Agenda Milliman and MG-ALFA Evolution of financial modeling Meeting the challenge Benchmark results 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)

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 YEQ2YEQ1Q3 Evolution of Financial Modeling  Modeling was an infrequent, “special” process –Annual cash flow testing –Pricing new products –Desktop software enabled actuarial independence and control

5March 31, 2009 YEQ2YEQ1Q3 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 YEQ2YEQ1Q3 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 YEQ2YEQ1Q3 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 YEQ2YEQ1Q3 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 YEQ2YEQ1Q3 Evolution of Financial Modeling There is a significant gap between the environment required and the environment that exists to support these requirements

10 Step 4 structure for sustainability 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) Step 5 build macro- model processes Step 6 automate and integrate Step 1 assess core actuarial projections Step 3 centralize, control, collaborate Capacity is a critical need Step 2 improve capacity

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

13March 31, 2009 Small In-forceLarge In-force Number of Servers Without Report With Report Without Report With Report (Elapsed run time in minutes) Runtime benchmarks

14 (Elapsed run time in minutes) Impact of fixed runtime components In-forceProcessingNo ReportWith Report FileTime1 Server20 Servers1 Server20 Servers SmallInput Build Send to Grid Work on Grid Move Results Merge Results Total LargeInput Build Send to Grid 0.1 Work on Grid Move Results Merge Results Total

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

16 * 1000 Scenarios were run for each test Calculation efficiency Number of Cell-Scenarios Per Hour Per Processor Core (in thousands) NumberLiability Model Points Servers200K20K2K

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