Robo-advice using algorithms are replacing financial planners

Robo-advice using algorithms are replacing financial planners

The next adviser who offers you a loan or investment opportunity might be a robot. And you could be really happy about that. You might trust them more than a human ­financial planner to deliver totally independent advice. Well, that’s your call.

But robot advice is part of a new financial marketing system that could chug along without much human intervention and is aimed at giving you a sense of financial security.

Robotic algorithms and computers can recommend home insurance, superannuation schemes, health insurance and property conveyancing — all without a human being within cooee. It’s done by selecting products based on the preferences of people in the same situation, and the same socio-economic group, who chose that particular product.

These robo-advisers are computer algorithms that can process and number-crunch vast amounts of financial market information to offer customers advice based on trending information. And they are getting smarter, thanks to ­machine learning and artificial ­intelligence.

You typically have to offer up a swag of personal details, but soon accessing them could be as simple as going online, opening a chat window and talking to a “chatbot” — a machine that emulates a human at a help desk.

The introduction of robo-­advisers is gaining momentum. This week American financial services provider Raymond James ­Financial said it would offer robo-advisers to clients by year’s end.

Last month Treasurer Scott Morrison was reported as supporting robo-­advisers for suggesting affordable superannuation advice.

Everyday Australians could be savvier financially with a robot coach on their side.

Private banking too is getting into the act with computers that offer financial advice to less wealthy people who can’t afford the human variety.

Robo-advice is becoming a crowded market in some parts of the world and it is not without controversy. Companies that offer it can find themselves in ­conflict with the traditional banking system.

This week, Britain’s Money Marketing reported that robo-­advice firm Scalable Capital had crossed swords with Bank of ­England governor Mark Carney, who said that automated advice could make markets more volatile. Scalable chief executive Adam French described Carney’s comments as “misinformed” and “throwaway”.

The available financial tools are likely to become more sophisticated with the accelerated use of artificial intelligence and machine learning. The ability of computers and systems to learn from humans and from vast amounts of accumulated data is accelerating in every facet of the digital world.

That includes learning colloquial language through inter­action with people in what’s called natural language processing. We’re starting to communicate more easily with smartphones and computers by voice as a result.

In the financial sector, machine learning is not only about harnessing financial data. Machines can be used to analyse the voice tones of customers to work out if they are happy.

National Australia Bank division UBank, which offers online banking services, recently told The Australian it was using IBM’s Watson supercomputer to implement what chief executive Lee Hatton calls “cognitive banking”.

“Watson has the capabilities to analyse both voice and text conversations to identify the tone and characteristics of a customer,” she says.

“From here, the AI creates a profile which understands them and ensures we can assign the team member that’s best for their needs, creating a more personal experience.”

The AI revolution also is creating opportunities for start-ups that are tapping into the brokerage and loans area and are planning further to inject AI into their operations. Australian start-up HashChing, which offers consumers an online marketplace for home loans, is a case in point.

Founder and chief information officer Atul Narang says the company’s website initi­ally offered 60 to 70 options for clients; it then started to prioritise rates for customers based on what previous customers had chosen nearby.

Initially the vetting occurred manually. But HashChing has started using machine-learning ­algorithms to identify “premium brokers” with a reputation for ­offering the best loans to clients.

“Before we were doing it manually — we were setting the brokers as premium brokers manually by looking at their performance; now the algorithm automatically does that,” Narang said.

“Now the algo­rithm automatically checks for the broker’s performance based on their review rating, the conversion rate and ­response time. It then automatically picks that broker as a premium broker.

“That has a direct impact on our business. We don’t want to burn our leads giving (business) to brokers who are not performing.”

HashChing is further implementing machine learning to ­adjust the broker conversion rate based on the present market conditions. If financial conditions are tough, the broker assessment can be more lenient.

The HashChing road map ­includes building a chatbot to ­handle customer inquiries. Work will start this month on the chatbot that will supersede a human in that role.

A human can still intervene if the chatbot cannot answer the question. “It will answer their questions and we’ll save on resources,” Narang says.

HashChing also has started building a predictive analytics tool that will tell customers which loan deals in their areas are trending, and on which days and timeslot. It’s like the Uberisation of loans marketing.

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Source: Robo-advice using algorithms are replacing financial planners – The Australian