Stochastic modeling is a complex, mathematical process that uses a combination of probability and random variables to forecast financial performance, or, in the case of reserve setting, to forecast financial requirements. The word stochastic comes from the Greek word that means "skillful in aiming". So the term refers to a process of tightly targeting a numerical probability or projected end result.

"Nested" stochastic models, as the name implies, are stochastic models inside of other stochastic models. They are not explicitly part of the principles-based reserve method, but since the setting of reserves and capital will be based on a stochastic valuation, earnings projections will require stochastic projections at each future projection date, across all scenarios. This means that nested stochastic models are needed to appropriately manage the business, price new products, project earnings, or measure risk. These models are not for the technologically challenged - a 1,000 scenario model with reserves and capital based on 1,000 paths at each valuation point for a 30-year monthly projection requires the cash flows for each policy to be projected 360 million times. Layer on top of this the desire too look at the implications of stochastic mortality or credit and we have introduced additional nested loops into the projections.

The ability to run these types of projections and analyze the resulting information will require significant changes in the hardware and software infrastructure at most companies. Ultimately, a solution for many of these challenges will involve grid computing (linking many PCs together under common control). Some companies are already running stochastic and nested stochastic projections on grids with as many as 1,500 PCs.