I just cannot see us getting to a point where a purely technical, algorithm-based, ML-driven product would be easily explainable to a retail investor. Certainly not in the short to medium term. Maybe it never will. Or perhaps I am just a bit of a stick-in-the-mud.
Where I see more viable opportunities is where data analysis can be used to help advisers create plans and approaches for clients.
Delivering appropriate advice
As a society, I believe we have a responsibility to enable appropriate financial advice to be delivered to a wider group of people. Due to the costs involved – because it is a regulated and complex space – in order to do that effectively, the underlying advice model must be able to be scaled.
To do that, you have two options.
You can either automate the advice process, giving people questions to answer and, via algorithms, enable them to self-serve. Or, the other route is by helping advisers become more efficient by automating many of their processes and tasks, freeing up time so they can deliver advice to more people at a lower price point.
Both of these things are happening currently, to varying degrees of maturity and success through ‘if this, then that’ flowchart style approaches.
However, if one were to apply ML, an analysis of historical outcomes for a group of investors could be used to train a ML algorithm. It could be taught to devise investment strategies for another group with comparable characteristics.
While their expected results are not guaranteed, it might lead to a fairly decent core model that might work for certain similar types of investors. Such a model might be used to benefit people in future, who would otherwise be excluded due to costs.
Critics could rightly argue over the bluntness of such a tool, but in my view solutions of this nature should never be pitched as a direct replacement for bespoke personalised advice. These techniques, in common with robo-advice, in general represent an alternative approach to support a wider group of people making financial decisions.
We would also nearly always advocate the option to introduce a degree of human intervention at some point in the process, such as in traditional robo-advice, when the ‘if this, then that’ protocol hits a proverbial wall.
Where I see a prime opportunity within the financial planning process would be around tax optimisation. For example, solving a specific technical challenge in a quicker and more efficient way using ML. But I see this very much working as one tool among many rather than a complete revolutionary overhaul.
Avoid one-size-fits-all approach
