Behind the scenes: What the AdviceBridge advice engine does and how it works
The advice engine is the most exciting thing about AdviceBridge and the element that sets it apart from other technology.
Ultimately, it can analyse a client’s holdings across general investment accounts (GIAs), ISAs, and pensions in much the same way that a paraplanner or adviser would. However, where it can often take many hours to do this manually, the advice engine can do it in a matter of seconds.
When you consider that a typical advice firm may take anywhere between 25 and 35 hours to onboard a prospective client, you can see the obvious advantage of the AdviceBridge advice engine, which reduces this to under five hours, saving more than 80% of the time.
This is because it has all the answers needed to make decisions for you.
The system is coded to understand:
The system can also:
- Create asset price simulations – using a stochastic model for asset prices
- Illustrate effects of life expectancy – through stochastic modelling
- Include State Pension benefits in recommendations
- Account for Child Benefit – calculates High Earner child benefit charge based on the client’s income, not their partners, and the impact on effective pension tax relief
- Advise that clients stop contributing so much to their pension as they’ll hit their Lifetime Allowance and thus divert money to an alternative instead
- Make choices when reaching retirement as to whether to buy an annuity (and if so what size) or utilise drawdown.
This is a simple overview of the capabilities of the advice engine. For a deeper understanding of everything it can do to make life easier, get in touch and ask for a demonstration of the system at work.
The starting point is information
Since the system has everything needed to analyse the information that it receives, the first job is to provide the information you’d like it to analyse.
Information can arrive in two ways:
- By drawing on existing client information from your back office
- Through the onboarding and fact-finding process.
Once the system has the information, it is ready to begin its analysis.
Analysing hard facts
The advice engine uses facts such as the size of assets, client age, desired retirement timeline and life expectancy, contributions, and cash flow requirements, including goals and retirement income, in the future.
As you might imagine, being a computer, the system can only analyse hard facts.
While it can absolutely gather soft facts as part of the information input, it can’t execute a decision that may be influenced by these. Instead, it will flag up any soft facts that may alter the final recommendation. This then allows you to review the soft facts and, if you change anything, re-run the recommendation producing a revised recommendation report automatically.
Checking every piece of information
When the recommendation is made, the system can check that every piece of information – whether it’s a hard fact or a soft fact – is included and the adviser then simply needs to confirm or adapt it.
The system is then able to re-run everything. Usefully, every time the engine re-runs something, it takes seconds as opposed to hours.
So, imagine the steps you’re used to:
Step one: Adviser meets with a client to carry out a fact-find and gather all the information.
Step two: Adviser and paraplanner sit down to discuss the findings and figure out the best route forward or shares a file of information that the paraplanner has to read and understand.
Step three: Paraplanner analyses all the information, potentially running cashflow analysis and different scenarios to produce the draft recommendation report.
Step four: Adviser reviews and checks the recommendations before presenting them to the client.
Instead of you or your team spending hours on fact-finding, inputting data, re-keying information, using templates, copying and pasting and sharing findings, the advice engine can do all of this within a matter of seconds – for every single client.
Imagine how much time you would save by harnessing the power from the advice engine.
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