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actuarial computing demands providing capacity through saas

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

agenda

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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

milliman and mg alfa
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
slide4

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
slide5

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
slide6

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
slide7

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
slide8

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
slide9

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

capacity is a critical need

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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

scalable cloud actuarial infrastructure scai
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)
seriatim policy test
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
slide13

Runtime benchmarks

(Elapsed run time in minutes)

slide14

Impact of fixed runtime components

(Elapsed run time in minutes)

stochastic policy test
Stochastic policy test
  • Test parameters
    • 2k, 20k, and 200k liability model points
    • Large in-force input size
    • With reports
  • 8 cores/server
slide16

Calculation efficiency

* 1000 Scenarios were run for each test

conclusions
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