One of the interesting differences in Australia is that it has compulsory pensions, meaning that there’s less discretionary money to invest. It is compulsory that all employees save 9.5% of their salary towards retirement.
The market that a traditional robo-advisor is after, which is discretionary investments, is small in Australia compared to other parts of the world, since there’s so much money going into the pension system. A number of Australian robos are focusing on that, helping young people get into investing or saving for a mortgage, and thereby offering financial advice at a fraction of the cost.
On the other side, we’re seeing pension funds looking at developing their own digital advice capabilities. Again, that trend has been going on for about six years—the difference is that now we’re calling it robo-advice, rather than just online advice.
Beyond that, several startups are focusing only on education and guidance aimed at helping younger people getting into investing, children learn about investing, or parents teaching their children about budgeting.
Several larger players are looking at adopting some of those approaches.
One interesting example is Acorns, which enables micro investing. Large pension funds either partner with Acorns or develop their own incremental savings, where that incremental amount goes into the pension fund rather than an investment vehicle. Recently Acorns has announced it will launch its own pension fund.
But that’s not all. When it comes to innovation, we’re certainly seeing artificial intelligence (AI) and predictive analytics being used more and more. A lot of the more innovative companies, either at the FinTech or the company end, are trying to personalize as much as possible.
Typically, people have worked on age-based segmentation, which is again very broad-brush. However, now they are realizing the benefits of collecting large amounts of data both internally and externally; they can use it in analytics and AI to be much more focused and personalized with respect to the target that they advise.
The current state of AI, machine learning, and data analytics
We’re still in the very early, probably experimental stages of these technologies; they are still fairly immature, and data analytics, particularly the actual data itself, represents a big challenge. There’s no shortage of data, but data in the right format, in the right structure, and at the right level of quality, is lacking.
Australian pension funds are no different from those in other parts of the globe, where they typically haven’t collected consumer and behavioral-type data in the past. They’ve been much more focused on age, date of birth, address, etc., and because in Australia the main distribution channel of the pension fund is the employer, the fund itself doesn’t have a very strong relationship with its clients.