The key competitive advantage of the PrimeSolve engine is the optimisation methodology that underpins strategy and product recommendations. PrimeSolve is able to optimise over 200+ strategy recommendations, 1000+ financial products & 100,000+ investment options to produce a combination or recommendations that are in a client’s best interests. 

This methodology creates unprecedented, powerful financial advice recommendations that are:

• Fast – comprehensive recommendations in seconds

• Robust – performs millions of simulations using multiple advice-related variables and constraints to find the best solution for a given set of financial objectives

• Accurate – based on Australian laws and regulations, clear logic and mathematical concepts

• Clear – able to be understood in terms of which recommendation relates to achieving which objective, and why

What is ‘optimisation’?
Optimisation can be defined as maximising or minimising a function (or goal), relative to some set, often representing a range of choices available in a certain situation. The function allows comparison of the difference choices for determining which combination of options is expected to provide the best outcome.

How does PrimeSolve’s optimiser work?
The model is a carefully defined set of inputs, resources, costs, assumptions, constraints, preferences and objectives/goals – expressed in matrices and logic statements that find the best solution to a financial advice problem.

The output is an optimised set of financial recommendations that encompasses:

• the best possible solution and why;

• it’s binding constraints; and

• the alternative simulations discarded and why.

Why optimise recommendations?

Optimisation is a key competitive advantage because:

• Optimisation is unmatched in its ability to extract maximum value with a rigorous decision-making process.

• Optimisation driven decision making is accurate, faster, cheaper and consistent.

• Optimisation is not just about software – it’s about changing the decision-making process, turning it into a science.

The value of optimisation models, algorithms and technology is twofold – it can help clients gain a better understanding of their finances whilst it generates the best possible plans to achieve their financial goals. And the combination of these two things is very powerful for any individual, or financial planning practice servicing individuals.

How do we know we have found the ‘optimal’ solution and not just a good one?

PrimeSolve’s solution is a linear optimisation problem. The ensures a global solution instead of a local solution. Optimization is the process of finding the point that minimises a function. More specifically:

A local minimum of a function is a point where the function value is smaller than or equal to the value at nearby points, but possibly greater than at a distant point. A global minimum is a point where the function value is smaller than or equal to the value at all other feasible points.

Generally, optimisation solvers find a local optimum. They find the optimum in the basin of attraction of the starting point. In contrast, the solver used by PrimeSolve is designed to search through more than one basin of attraction. In practice, what this means is that PrimeSolve has been built to scan for all optimal possibilities, not just local optimal possibilities. This ensures that there are no possible better solutions for a given set of inputs and constraints. 

Multi-year optimisation

PrimeSolve’s optimiser has the ability to optimise over multiple years rather than on a year by year basis.

This can have a significant impact on a person’s strategy – for example, in a year by year optimisation it would be rare for it to be optimal to purchase an asset such as a property as the initial transaction costs would result in a negative outcome over 1 year. However, the transaction costs may be absorbed through growth and rental savings over time, therefore the property transaction may in fact be optimal if over a 10-year timeframe. 

Other decisions such as superannuation contributions, debt repayments and pension commencements need to be viewed in context of a longer perspective to ensure the optimal course of action is taken and ensure the strategy does not disadvantage the client in future years. By looking forward PrimeSolve’s engine can handle this kind of problem easily, whilst other solutions that only offer year by year optimisation may not be able to zone in on the correct outcome.

Multi-objective optimisation

Multi-objective optimisation (also known as multi-objective programming, vector optimisation, multicriteria optimisation, multiattribute optimisation or Pareto optimisation) is an area of multiple criteria decision making that is concerned with mathematical optimisation problems involving more than one objective function to be optimized simultaneously. Multi-objective optimisation has been applied in many fields of science, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives. Minimizing cost while maximizing comfort while buying a car, and maximizing performance whilst minimizing fuel consumption and emission of pollutants of a vehicle are examples of multi-objective optimisation problems involving two and three objectives, respectively. In practical problems, there can be more than three objectives.

Competing client objectives
In the context of optimising a financial plan, it is probably that a client may have multiple competing objectives which may not necessarily be achievable simultaneously. In this scenario, the optimisation problem must be able to prioritise which objective should be prioritised.

PrimeSolve solves this issue via the introduction of a penalty for goals not met. The more important the goal, the higher the penalty that will be applied upon the objective function. 

The net outcome is that the engine will prioritise achieving those goals that are most important to the client/s.