What modelling concepts are available?
Financial modelling software typically requires the adviser to insert their own recommendations into the financial model. A trial and error process is required to visualise the benefit of each recommendation, and the model often fails to capture the range of possible outcomes. Whilst an adviser driven pre-determined approach is still available within the PrimeSolve platform, we address some of the limitations of basic deterministic modelling by introducing both a optimisation and a simulation model type.
Optimisation is used to automate strategy and product recommendations whilst ensuring consistent and precise results . Simulation is used to measure the range of potential outcomes and provide valuable insights around risk .
Model types
The PrimeSolve platform allows the adviser to produce the following modelling options:
| Model | Description |
|---|---|
| Base model | The purpose of this model is to project a client scenario based on the assumption that they continue on the same path that they are currently on. No optimisation decisions are applied. |
| Deterministic model | This model does not employ any optimisation techniques. Advisers can build on the base model by inserting recommendations into the model to improve the outcome. |
| Optimisation | The purpose of this model is to determine the optimal course of action for a given set of client objectives using PrimeSolve’s proprietary optimisation software. All cashflow/transaction decisions are based on what produces the best outcome for the given inputs. |
| Simulated deterministic | This model runs a deterministic model with 1000 trials via a Monte Carlo simulation. The investment return is made uncertain based on the mean and standard deviation of the asset class. This model enables you to generate data around probability of outcomes such as net equity, ability to meet objectives, income in retirement etc. |
| Simulated optimised | Similar to simulation (deterministic), this model runs a Monte Carlo simulation. However, unlike the deterministic version the optimised simulation is able to make optimal decisions in the face of uncertainty. However, the optimised results are not linear. Therefore decisions will improve the model, but not necessarily find the best global solution . |
| Optimised simulation | This model is able to perform both optimisation and simulation simultaneously, whilst remaining linear. This allows the model to make optimal decision in the face of uncertainty, such as: “What action gives me the highest probability of meeting my retirement goals”? |
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