The wealth management industry should view technology as a supplementary tool rather than a threat, industry experts maintained at a recent event held by this publication.

Relationship managers are not going anywhere and should not feel threatened by the rise of fintech organisations or robo-advisors, panellists unanimously declared at a recent WealthBriefing breakfast briefing in London.

Last month, Andy Haldane, chief economist of the Bank of England, warned that 15 million jobs in Britain were at risk by automation. There's a lot of talk of “disruptive” technologies in the wealth management industry as robo-advisors and automated investment services continue to pop up. Firms have had to adapt to a new playing field and spruce up their digital capabilities to include features such as algorithm-based risk assessment and automated portfolio selection. Robot paranoia aside, this seems to be one of the industries where the “personal touch” is reassuringly necessary. 

Chaired by Bruce Weatherill, chairman of WealthBriefing, the panel consisted of: Steven Light, digital private banking at Coutts; Huw Kwon, head of data science centre of excellence for UK & Ireland at Accenture; Thomas Lack, chief operating officer at Brewin Dolphin; Verona Smith, head of platform at Seven Investment Management; and Duncan Ash, director of financial services at Qlik, which sponsored the event.

Embedding digital services at the heart of a wealth management firm can be complicated. There is a lot of value for a wealth management firm in just sitting down with private bankers and helping them see the power of their digital proposition, said Light. This, in his experience, has proven instrumental in making sure that they really "get it" – that technology is not a replacement for human one-to-one relationships.

“It is by way of such conversations that I have seen private bankers – perhaps traditionally seen as being averse to digital channels of communications – switch to embracing the opportunities presented by technology,” he said.

Kwon shared with the audience an example of a recent project where he used robotic technology and natural language processing to scan and process forms, just as a human would do within an organisation’s customer relationship management (CRM) system. This, he said, has saved relationship managers time to focus more on the relationship. 

“Such technologies should not necessarily be viewed as a 'disruptor' but more as an empowerment tool for RMs,” he said.

Basically, digital touchpoints are becoming a go-to channel for high net worth clients so data analytics and relationship management are not mutually exclusive; they depend upon each other to open up revenue opportunities. 

“Wealth management to us is still very much a people led business. We record what we do through technology but we are not governed by it,” said Lack.

Is there a difference then between having data and insight? Absolutely, the face-to-face time between the discretionary wealth manager and the client is the real differentiator, panellists agreed.

“It’s about the service but what we’re trying to do is supplement that so as to improve the entire experience and make the wealth managers much more efficient in serving their clients,” Smith said.

Devil is in the detail
Big data – a blanket term for the large or complex data that inundates a business on a day-to-day basis – has for some time been floating around in the whirlpool of wealth management buzzwords. 

But it’s not about big data, it’s about finding that right information you need to better serve the client, said Ash.

According to Kwon, it is the tiny pieces of data that together reveal behavioural patterns and present opportunities for web page optimisation. He refers to all such data as digital “bread crumbs” or “footprints”. The trick is to know the context, otherwise they have little value. For instance, the bigger picture may include when the client logged in, what investment product they are looking at and how long they are on the page. From these customer journeys, we can identify an individual's intent and reason for engagement, Kwon said, and then trace these journeys to actual outcomes, such as purchase decisions or to an outcome value such as profit.

“This process can be applied in various client segments, such as high net worth, ultra-high net worth, but the biggest cost savings and service quality enhancement will be realised on the mass affluent market,” he said.

How far is too far?
A new generation of wealth is emerging and they are used to spending a lot more time in cyber space than their less tech savvy, older peers. Smith highlighted research that shows nowadays people reveal more about themselves online because they feel there is this comforting barrier; that they are not actually talking to someone.

“Ten, 20 years ago, whatever the bank manager said was gospel. Now, in the age of social media, people are looking horizontally for advice and opinion. So the value lies in understanding your client and where they are going to go in the digital world,” said Smith.

Such readiness to divulge personal information online brings questions of privacy and morals: How far should you be able to go with all the data you gather? Is there a line that should be drawn? If so, where is it?

“What relationship managers have over robo-advisors is brand equity and trust – and they don’t want to lose that. As far as where they line stands, it’s a moral issue and one of credibility because if you go too far, you’re going to lose the trust of your clients. You have to think very carefully about how to use the data to avoid overuse; only use it where it is actually practical,” said Ash.

“There comes a point where it becomes too personal and there’s where you need to stop. That is down to the discretion of every business.”

Kwon called attention to the principles of reciprocity by which banks agree not to abuse data and instead use it in a fair, balanced way. For example, a bank should not target customers with negative performance data, such as defaulting on a loan; they should instead use that data to target them for loan offer, he said.

“Data science should be used to make prediction models or similarity models; you wouldn’t communicate granular findings to your clients. In other words, use the data for the good of the client to understand, for example, what kind of investment products would interest them, rather than as a short-term acquisition opportunity,” he said.

Wealth managers must not take for granted the level of trust clients place in them, stressed Light. 

“To ensure we preserve this data advantage, we must continue to deliver on security, understand what it is that is interesting and valuable to the individual client and demonstrate how specific insights may benefit them. That, in itself, leads to further data points being disclosed, which starts the data-insight cycle all over again,” he said.

Smith added that there is a misconception that high net worth clients are not as responsive as others. But they are, she said, so not only do wealth managers need to be quick in getting valuations and updates across, but also innovation is key.

Of course, innovating is neither an easy nor cheap task. It requires experimentation, perhaps an innovation lab where you can test data. People in many different roles need to be given the opportunity to innovate, and this can only happen when they are provided with the appropriate data and tools. It cannot be achieved in isolation; it requires organisational or cultural change, said Ash.

What’s more, the cost-benefit analysis when deciding what to try out is hardly straightforward, according to Lack. A lot of the benefits are not easily quantifiable upfront so it’s not always easy creating a standard MVP (minimum viable product) with the highest return on investment versus risk, he said. 

“It’s really about assessing whether the product is something that is really needed and you can work out some form of anticipated payback.”

The struggle here is that clients may not get on board with a new product or service unless they have a specific outcome in mind, and within a specific timeline. Light talked about the need to try things on a small scale, test it and learn from it before investing too heavily. Working with small start-ups on prototypes, often at low cost or for free, can be a great way to prove a concept before investing fully.

Also, it is the digital age that arguably makes the outcome economy possible. Kwon emphasised that an organisation should have all digital efforts mapped to value outcomes through a set of key performance indicators in order to identify the issue, measure its current scale and then decipher to what extent it can be solved by whatever transformational project you have planned. Ultimately, the end outcome should be somewhat measurable to substantiate whether something is likely to work or not, he said.

“Data may be a headache and a challenge for most of us but it is where a lot of the value is locked – in its accumulation and organisation. From a business perspective, this is where the profit lies,” Kwon said.