Because of existing and upcoming regulations, insurers perform quite a bit of analysis over their assets and liabilities. Actuaries need time to review and correct results before reviewing the reports with regulators. Today, it is common for quarterly reporting to require thousands of hours of compute time. Companies which offer variable annuity products must follow Actuarial Guideline XLIII which requires several compute intensive tasks, including nested stochastic modeling. Solvency II requires quite a bit of computational analysis to understand the Solvency Capital Requirement and the Minimum Capital Requirement. International Financial Reporting Standard 17 requires analysis of each policy, reviews of overall profitability, and more. Actuarial departments everywhere work to make sure that their financial and other models produce results which can be used to evaluate their business for regulatory and internal needs.
With all this reporting, actuaries get pinched for time. They need time for things like:
- Development: Actuaries code the models in their favorite software or in custom solutions. Anything they can do to reduce the cycle of Code-Test-Review helps deliver the actuarial results sooner.
- Data preparation: Much of the source data is initially entered by hand. Errors need to be identified and fixed. If the errors can be found and fixed quicker, the actuaries can be more confident in their results.
- Result verification (and re-run when needed): Once the math and other algorithms have run, the actuaries review the resulting reports. Actuaries regularly review intermediate results during a longer modeling run. A few results will look wrong, necessitating review of the corresponding inputs and intermediate results. Upon review, a few inputs get fixed and parts of the model are run again.
Most actuarial packages on the market support in-house grids with the ability to either extend the grid to Azure, or to use Azure as the primary environment. As part of the move to Azure, actuarial teams review their models and processes to maximize the scale benefits of the cloud. For example, to reduce the amount of clock time to run a model, the insurer will continuously update data stored in Azure. The insurer will also work with their software vendors to review the model for changes that would allow more of the tasks to run in parallel. When running thousands of scenarios, it is frequently possible to run the data as thousands of independent processes with an aggregation step performed at the end.
Recommended next steps
To get more time as an actuary to review results and build models, read the Actuarial risk analysis and financial modeling solution guide. The solution guide will teach you how to prepare your data, run your models, and review your results. The advice works with both your purchased software and the software that your development team wrote.