Actuarial Computing Demands Providing limit through SaaS .


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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.
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Page in view of Title Slide from Slide Layout palette. Plan is 2_Title with realistic. Title content for Title or Divider pages ought to be 36 pt titles/28 pt for subtitles . Moderator box content ought to be 22pt. DATE content box is not on ace and can be erased. The date ought to dependably be 18 pts. Actuarial Computing Demands Providing limit through SaaS Presented by Van Beach, FSA, MAAA MG-ALFA Product Manager October, 2010

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Page in light of Title and Text from Slide Layout palette. Outline is 1_Title with photograph Subtitles are Part of Title Field, then Modified Manually (see next page) Agenda Milliman and MG-ALFA Evolution of monetary demonstrating Meeting the test Benchmark comes about

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Milliman and MG-ALFA Milliman is a worldwide actuarial counseling firm with more than 50 workplaces overall MG-ALFA is a money related projection framework utilized by statisticians for evaluating, hazard administration, and administrative reporting Currently 111 MG-ALFA customers 193 establishments all around 120 US Dominate US Market (New & Existing Clients) Clients in 20 Countries 2000+ MG-ALFA customer clients Milliman experts are likewise customers

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YE Q1 Q2 Q3 YE Evolution of Financial Modeling was a rare, "uncommon" process Annual income testing Pricing new items Desktop programming empowered actuarial freedom and control

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YE Q1 Q2 Q3 YE Evolution of Financial Modeling The models have turned out to be more perplexing Dependent obligation and resource projections Stochastic examination (settled stochastic for estimating) Products and organization rehearses more entangled More granularity to catch policyholder conduct and other hazard qualities

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YE Q1 Q2 Q3 YE Evolution of Financial Modeling Models are at the center of more capacities and investigations CFT, valuing, guideline based holding, arranging ALM, EC, C3 Phase 2, C3 Phase 3 GAAP, IFRS, Solvency II, MCEV, EV Analysis regularly requires running a few models under reliable bases and acclimatizing comes about

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YE Q1 Q2 Q3 YE Evolution of Financial Modeling Models and investigations are required all the more every now and again Semi-yearly financial capital Quarterly installed esteem, arranging, ALM Monthly rule based saves Daily supporting

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YE Q1 Q2 Q3 YE Evolution of Financial Modeling Models are conveying mission-basic data Reporting windows are more tightly Increasingly seen as a component of the "creation" prepare More clients included and more purchasers of model results

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YE Q1 Q2 Q3 YE Evolution of Financial Modeling There is a huge hole between the earth required and the environment that exists to bolster these prerequisites

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Page in view of Title Only from Slide Layout palette. Outline is 01_Title with photograph. Subtitles are Part of Title Field, then Modified Manually (see next page) Capacity is a basic need Step 1 survey center actuarial projections Step 2 enhance limit Step 3 bring together, control, work together Step 2 enhance limit Step 6 robotize and coordinate Step 5 construct full scale demonstrate forms Step 4 structure for manageability

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Scalable Cloud Actuarial Infrastructure (SCAI) Multi-center neighborhood desktop PCs Private mists (i.e., in-house frameworks) SaaS (e.g., R Systems) PaaS (e.g., Azure)

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Seriatim strategy test pilots Size of the contribution (in-compel) record. Size of the outcome document. The quantity of servers. Test parameters 4 million approaches Large in-drive input size is 10* little In-constrain With and without reports 8 centers/server

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Runtime benchmarks (Elapsed run time in minutes)

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Impact of settled runtime parts (Elapsed run time in minutes)

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Stochastic arrangement Test parameters 2k, 20k, and 200k risk display focuses Large in-compel input measure With reports 8 centers/server

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Calculation proficiency * 1000 Scenarios were keep running for every test

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Conclusions R Systems gave a profoundly adaptable figuring environment for MG-ALFA Calculations were near straightly versatile Data development/handling time was altered, subsequently making unavoidable losses as assignment size diminished MG-ALFA is effectively reconfigured to change errand estimate Optimize effectiveness or Optimize runtime

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