How machine learning can improve adviser efficiency

The latest data shows there are now just 15,688 advisers on the Financial Adviser Register (FAR), yet the number of Australians in need of advice is ever-increasing.

According to the latest Investment Trends Adviser Technology Needs Report from July, the average number of active clients per adviser has risen to 120, which has increased from 113 a year ago.

“The average number of clients serviced by any single adviser appears to be (slowly but surely) approaching Dunbar’s cognitive limit,” said Dr Irene Guiamatsia, head of research at Investment Trends.

“This renders considerations of scale, digital engagement, and the broader role of technology more pertinent than ever.”

According to Greg Hansen, executive, group strategy at HUB24, the key to keeping up with the expansion of client books is taking advantage of every digital tool available.

“A key metric that we’re interested in is 120 clients per planner. That’s obviously a result of all the regulatory requirements and the like, and we’re interested in ways that we can increase that,” Mr Hansen told ifa.

“Through the platform we can get some increases, but with our entire ecosystem between the platform and our data and technology business, and myprosperity and Class, we should be able to drive that figure higher.

“Now, part of that is about digital advice. But there’s a range of other technology solutions as well, that we think we can bring to planners to increase their capacity to see more clients.”

Looking towards possible changes being ushered in through the government’s response to the Quality of Advice Review, he added if statements of advice become less prescribed, it opens them up to more innovation.

“The adviser can use their professional judgement about how they present that advice to the client, but they still need to be able to demonstrate that they’ve acted in the client’s best interest,” Mr Hansen said.

“We think there’s a range of tools, and it might be to do with voice transcription and speech analysis and machine learning that the adviser can then use to demonstrate best interests.

“So, can you generate some efficiencies by bringing technology to that process, so the adviser can use their professional judgement, but they’re also covered in terms of best interests?”

On the HUB24 front, Mr Hansen said the business is hard at work looking for ways that machine learning can reduce the cost of the core functions of licensees.

“The problem that we identified was lack of access to quality data, so we’re bringing machine learning expertise to that problem,” he said.

“Then if we’ve got that data, we can extend that to adviser use. So, benchmarking and identifying cost to serve and identifying areas for improvement, those sorts of things.

“Our innovation lab is working on speech analysis and machine learning to improve the productivity of the file noting process and the follow-up process for meetings.”

Stressing the importance of finding productivity increases through the streamlining and digitisation of core tasks, Mr Hansen said advisers are still in a strong position.

“The fundamentals are that you’ve got a massive, massive demand and no supply, so advisers are in a great position,” he said.

“But how do we help them drive productivity and increase the capacity to see more people so that more Australians can get advice, and at the same time, we can reduce the cost of providing advice as well?

“Supply’s not going anywhere in the short term, demand is really high. From our perspective, that productivity element is the real key in the short term.”