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THE PROBLEM

One of our clients approached us with a problem familiar to many. Their simple modelling environment had met their needs for several years, but their needs had grown. They faced the two challenges of performing stochastic business planning activities in a timely and affordable manner, and in also finding a way to make their model development and management more effective to prepare for that.

The client had… And the client needed…
A deterministic liability model A stochastic asset-liability management (“ALM”) model with manual and automatic management actions for bonus and investment decisions
Limited model developer resources The modelling capability to develop their sub-models in line with their functional specification and local regulatory requirements
No economic scenario generator (“ESG”) Stochastic economic scenarios to provide a market-consistent value for the investment guarantees in the business
On-premise hardware (desktop computers) High performance computers to provide the necessary speed and memory to run all of the business, policy-by-policy over many scenarios

OUR APPROACH

Our ultimate solution involved a deployment of open architecture software tools integrated with flexible computing technology and supported by actuarial consulting services. 

The four pillars of the solution:

1. Migrating the client’s existing MoSes model to Willis Towers Watson’s RiskAgility FM

RiskAgility FM is an open architecture modelling platform, which was essential for creating a bespoke solution, building off the existing models for the liabilities. As a first step, we moved the client’s models onto RiskAgility FM with no changes to the peripheral input and output processes allowing us to reconcile results back to those from the original models with relative ease.

Once the model code was optimised for RiskAgility FM, the development team could work faster with a modern interface, running on Microsoft Visual Studio technology, harnessing IntelliSense for syntax highlighting, debugging and code completion.

2. Developing the RiskAgility FM model using RiskAgility Team Edition

Another advantage that RiskAgility FM gave us was that we could get multiple developers working on different aspects of the model simultaneously without creating downstream risks and inefficiencies during the code merging processes.  Working in RiskAgility FM Team Edition meant having a centralised, working repository for sharing, review and testing of models, a control system for access permissioning and model versioning and a comprehensive range of compare, merge and conflict resolution tools.

RiskAgility FM Team Edition runs on Microsoft Foundation Server thus benefiting from market-leading, ‘tried-and-tested’ technology for source code management.

3. Providing economic scenarios calibrated in Willis Towers Watson’s STAR ESG

Obtaining a market consistent value of options and guarantees was paramount so Willis Towers Watson’s risk neutral (“RN”) ESG was selected from our STAR ESG suite.  STAR RN was used to project simulations for major asset classes, such as interest rates, equity, property, credit, exchange rates and inflation, on a risk neutral basis for use in the stochastic projection model.  Our experience with producing scenarios for the local market allowed us to quickly prepare a suitable calibration and validate this using STAR ESG’s built-in routines.

4. Connecting the RiskAgility FM model to Willis Towers Watson’s vGrid

The final piece of our solution was to give the company a flexible, scalable option for distributed processing so they can generate fast, precise results as and when they need them.  Given the models’ enhanced sophistication, stochastic capabilities and increased granularity a more substantial computing infrastructure was needed.  By using vGrid – a Willis Towers Watson service for RiskAgility FM which connects models to cloud computing in Microsoft Azure – we optimised core allocation and available memory to bring elapsed runtime down significantly.

Our vGrid service provides a ‘pay-as-you-go’ alternative to major hardware investments and the inevitable redundancy of on-premise compute resources outside of peaks in demand.  Moreover, Microsoft Azure data centres utilise a ‘defence in depth’ strategy to provide the highest level of security.

THE RESULT

By introducing these new technologies, the client achieved their goals:

The client needed… So our solution was to…
A stochastic asset-liability management (“ALM”) model with manual and automatic management actions for bonus and investment decisions Migrate their existing sub-models to Willis Towers Watson’s RiskAgility FM, allowing future model development to take advantage of the features of our latest platform
The modelling capability to develop their sub-models in line with their functional specification and local regulatory requirements Develop the RiskAgility FM sub-models into stochastic ALM models using RiskAgility Team Edition to facilitate a multi-developer working environment
Stochastic economic scenarios to provide a market-consistent value for the investment guarantees in the business Provide risk neutral economic scenarios calibrated in Willis Towers Watson’s STAR ESG customised for the local market
High performance computers to provide the necessary speed and memory to run all of the business, policy-by-policy over many scenarios Connect the RiskAgility FM model to Willis Towers Watson’s vGrid to access scalable, on-demand computing

The final modelling solution now makes possible a wide range of investigations and analytics. RiskAgility FM and vGrid ensure that this can take place in a robust and scalable modelling environment backed by the latest computing technology.