The field has been winning wealth management fans and generating plenty of noise. To make its insights bear fruit, lots of data needs to be handled. That means technology – lots of it. We talk to a figure working in the space.
There’s a convergence between the field of behavioural finance and Artificial Intelligence, as wealth managers use technology to digest data that new investment ideas generate.
As already reported here, behavioural finance is gaining wealth sector fans. The term applies to understanding how people mistake portfolio gains for pure skill rather than also accept the role of chance, or treat losses more emotionally than they do with gains, and follow crowd behaviour. These insights draw on views about how humans have evolved from pre-history, and are used to explain events such as stock market booms and busts, or share trading frenzies such as the GameStop affair in the US more than a year ago, or the regular gyrations of bitcoin. The pandemic, Russia’s invasion of Ukraine and spike in energy prices have given plenty of reasons for emotions to hold sway in markets.
The hope is that, armed with these insights, people will make fewer mistakes. And they can apply ideas to tech tools to provide “guard-rails” around their portfolios. That, at any rate, is the hope.
As far as Edinburgh-based Sonia Schulenburg of Level E Research is concerned, behavioural finance is an important field. But without the necessary tech tools, it is hard for investors to use the area’s insights wisely and achieve their goals.
“I think it [AI] is empowering people….it is going to automate processes….a lot of routine activities that we have to do every day, reporting, gathering data, looking for similarities and looking for solutions,” she told this news service in a call.
“From the Spiderman movie, we can say that `with great (high-processing) power comes great responsibility.’ I love this quote. Computers and good algorithm design is allowing us to do hyper-customisations that we could not dream of doing years ago. Clients have many different objectives and constraints and traditionally clients are placed in three to seven buckets depending on their age and risk preferences. This has to change. We have AI sufficiently developed to allow unique investment solutions driven by proper client optimisations,” she said.
One of the main advantages is that such technology can save managers’ time and allow them to focus more on other client needs, Schulenburg continued.
A big challenge for AI is how systems can offer explanations for
the decisions taken. “Financial markets are very dynamic systems
and you need technology that can explain decisions,” she