Actuarial Computing DemandsProviding capacity through SaaS

Presented byVan Beach, FSA, MAAAMG-ALFA Product Manager

October, 2010

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Agenda

Milliman and MG-ALFA

Evolution of financial modeling

Meeting the challenge

Benchmark results

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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

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YE Q2 YEQ1 Q3

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 March 31, 2009

YE Q2 YEQ1 Q3

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

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YE Q2 YEQ1 Q3

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

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YE Q2 YEQ1 Q3

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

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YE Q2 YEQ1 Q3

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

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YE Q2 YEQ1 Q3

Evolution of Financial Modeling

There is a significant gap between the environment required and the environment that exists to support these requirements

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Step 4 structure for sustainability

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

Step 2 improve capacity

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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)

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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 March 31, 2009

Small In-force Large In-force

Numberof Servers

WithoutReport

WithReport

WithoutReport

WithReport

1 43.5 81.8 47.9 94.7

5 11.3 28.6 17.1 33.9

10 7.5 23.2 12.4 31.2

15 6.7 19.7 10.9 23.4

20 6.2 19.1 10.8 22.7

(Elapsed run time in minutes)

Runtime benchmarks

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(Elapsed run time in minutes)

Impact of fixed runtime components

In-force Processing No Report With ReportFile Time 1 Server 20 Servers 1 Server 20 Servers

Small Input Build 2.5 2.6 2.5 2.6 Send to Grid 0.1 0.1 0.1 0.2 Work on Grid 40.8 3.3 67.7 3.8 Move Results 0.1 0.2 8.9 9.6 Merge Results 0.0 0.0 2.6 2.7 Total 43.5 6.2 81.8 18.9

Large Input Build 6.7 6.7 6.4 6.6 Send to Grid 0.1 0.1 0.1 0.1 Work on Grid 48.5 4.2 85.7 4.5 Move Results 0.1 0.1 8.4 8.9 Merge Results 0.0 0.0 2.6 2.6 Total 55.4 11.1 103.2 22.7

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Stochastic policy test

Test parameters– 2k, 20k, and 200k liability model points

– Large in-force input size

– With reports

8 cores/server

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* 1000 Scenarios were run for each test

Calculation efficiency

Number of Cell-ScenariosPer Hour Per Processor Core

(in thousands)Number Liability Model PointsServers 200K 20K 2K

1 204 203 132 2 201 202 126

50 172 160 55 125 146 115 31

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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