Technology and next generation advice

Emerging technologies are changing the way organisations, particularly superannuation funds, conduct customer conversations and provide financial advice.

The falling cost of computing services (cloud services), availability of a broader range of information sources (Big Data), artificial intelligence (AI) and machine learning (ML) are heralding new opportunities for organisations in how they conduct two-way customer conversations.

These advances are allowing the automation of non-routine cognitive tasks at an alarming rate; codified rules can be combined with pattern recognition and structured and unstructured data to deliver targeted and relevant customer observations in real time. All while advances in user interfaces are enabling computers to interact with people in ways that would have been considered science fiction only a few years ago.

There are commercially available technologies today that can provide virtual assistant capabilities. In fact, many financial services organisations have been benefiting from virtual assistant technology on the web for years.

Web-based virtual assistants enable two-way customer conversations and can enable transactions using a natural language interface. These intelligent systems deliver customer self-service solutions that recognise the customer's relationship and past transactions; they are easy to use, accurate and consistent, and delivered at a fraction of the cost of traditional call centre operations.

A clear application of this technology is in the provision of scaled advice

An ageing population with increasing retirement balances is creating a surge in the demand for financial advice in Australia.

This demand is also shifting to simpler, more targeted advice and customers are turning to their superannuation funds to meet this need. In recognition of this shift the regulator some years ago developed scaled advice, which is advice that relates to a specific area of a customer's needs.

The benefit of scaled advice is that it can be tailored to the individual circumstances of a customer, where general advice cannot without incurring the more onerous obligations required under personal advice (i.e. a full and detailed fact-find to satisfy best interest duties). Scaled advice makes the advice process simpler and more cost effective to provide.

The simplified obligations of scaled advice align nicely with technology-enabled customer conversations

Most providers are currently supporting scaled advice with para-planners who need to be trained and managed. They generate leads through (at times clumsy) referral processes and manage the entire engagement through costly and ineffective call centre-style practices that create a number of hurdles to fulfilment.

Successful scaled advice requires a guided customer conversation that defines the boundaries of the advice, collects and incorporates the circumstances of the customer relevant to the advice provided, and delivers this (including fulfilment) in a single engagement to avoid leakage in what can be a complex process.

This obviously includes the triage of customers needing a full advice engagement.

This is where a technology-enabled guided customer conversation can add real value. An interface supported by simple AI and ML could easily identify the scope of the advice required, ask the relevant questions to facilitate the appropriate fact-find, provide the advice (SOA) and enact the advice provided with a degree of compliance and at a scale not otherwise achievable 24/7.

It should not be a surprise that in their often quoted 2013 research into jobs likely to be computerised, Oxford University identified that personal financial advisers have a 58 per cent chance of being replaced by computers, largely due to their emerging ability to replicate non-routine cognitive tasks. The tools to make this a reality are available now.

But it's not just about providing cheaper advice 24/7, it can also be better advice

Many people's immediate question is 'Who would trust their financial future to a machine?' The answer is that it's all in the execution, and that most people should.

Combined with other emerging technologies, AI and ML open up a range of opportunities to create effective and engaging customer experiences, delivering better advice more consistently to more customers than is possible with call centres filled with para-planners.

Using AI and ML allows you to enhance codified advice and combine it with other customer data (including Big Data). This combination allows the nuances of a customer's responses to be incorporated in real time with advice models, broader data sources and learning from prior advice engagements. This can be achieved with greater consistency, accuracy and timeliness than expected under more traditional methods of advice delivery, all while managing the boundaries of scaled advice and triaging customers who need a full or face to face advice engagement. Managed online, this process could also facilitate frictionless fulfilment, i.e. immediate enactment of the advice given.

Super funds and advice

Most superannuation funds are facing a changing advice landscape. The demand for scaled advice is increasing, but the costs and compliance obligations of existing advice processes can prove difficult, particularly for large direct or default superannuation funds.

Providing scaled advice may be the answer to customer engagement and promoting retention, particularly as customers reach retirement. Too often the debate is focused on 'Would you trust a machine to provide financial advice?', but current and emerging technology is enabling some very interesting opportunities to provide quality scaled advice with a cost profile acceptable to members, all the while triaging customers with complex needs.

Taking steps to explore the possibilities in this space, rather than ignoring its rising prevalence, will ensure that you are not left behind. It could also provide the building blocks for doing something truly groundbreaking (see 'A glimpse into the near future' below).

   Brendan Daly is a practice partner at Wipro.