This is chapter two of a research report issued by this publication with EY and Finantix, entitled Applying Artificial Intelligence in Wealth Management: Compelling Use Cases Across the Client Life Cycle.
Tom Burroughes, Group Editor at WealthBriefing, explores how AI technologies can help wealth managers ensure they always have a full pipeline of future business and that they engage with those prospects as effectively as possible. (To view the full report, and see the link to register for the copy, click here.)
In today’s ever more hotly-contested market, private client advisors know only too well how important it is to have a full pipeline of future clients to expand business and replace customers who move away. And they need to know that once they’ve figured out who potential new clients might be, they also need toolsets made available by AI and other technologies to reach out to such people as precisely and persuasively as possible.
AI can help managers connect with new clients by finding out where they are, alerting practitioners to liquidity events and who stands to benefit from them by highlighting Initial Public Offerings, trade sales, share options being exercised, significant bequests and even legally-mandated financial pay-outs. The science of sifting liquidity events is developing and has moved fastest where publicly-listed firms are concerned, but there are also developments on the private side of the street.
Discovering leads about new clients, and managing the initial approach and the onboarding process, can entail a number of challenges, however, including where foreign language and other potential barriers to understanding arise. Once the potential new client is discovered, and then approached, there is also the need to get an early and accurate read on that individual’s needs and position. Any data analysis that can make it easier to gauge the full balance sheet of a client – the liabilities side as well as assets – is clearly going to be a huge competitive advantage for wealth managers.
Finding and keeping clients
As Alessandro Tonchia, Co-Founder of Finantix, describes, AI technologies can provide elegant solutions to many prospecting challenges, helping the early part of the client discovery process to proceed along very efficient lines.
“The sequence is that you find a potential lead – someone who owns a fast-growing company, for example – and you derive a client profile to assess if it fits your qualification criteria,” he said. “Then the technology can score which of your bankers is most attuned to that profile, as they speak the same language, are the same age, share interests or because they already have many similar clients in that field.”
To further foster a connection, Tonchia explained that an institution can then scrutinise a source such as LinkedIn to see if the relationship manager is already tangentially connected to the target, and work out if some existing clients sit on the same company boards as them. AI can even help produce the right message and the appropriate language to reach out to a target in, he said.
As the information pool deepens, AI and machine learning techniques can massively boost client acquisition, client retention and sales, confirmed Phil Tattersall, Director in EY’s Wealth & Asset Management Data and Analytics advisory practice.
“Alternative data sources – such as social media, mainstream news and relevant publications - can be powerfully leveraged and analysed to generate new insights for prospecting and compiling prospect profiles,” he said.
Similarly, clients can be segmented and analysed for retention risk (for example by correlating transaction and channel data with market events to reveal a client’s true risk tolerance) and the relevant corrective actions initiated.
Working out early in a conversation how easy or hard it will be to retain a client’s business in certain conditions is surely extremely useful for a firm that wants to know how consistent its revenue from certain areas of business is likely to be.
The fine detail that AI can unearth about clients can empower advisors, giving them an edge in making pitches to new prospects. Private banks are certainly on board with the use of AI in the way they understand and support clients throughout the relationship and don’t see it as taking the human out of the equation, but rather as augmentation for people skills.
“Elements of AI and robo-advice can be combined with the wealth of data that is available to help us understand clients’ risk profiles, to build our investment themes and to enable our front-office teams to become even more proactive managers on their behalf,” said Jack Oliver, Head of Digital at HSBC Global Private Banking.
Senior managers at Royal Bank of Canada’s wealth management operation in the UK, for example, have stressed to the writers of this report how full use of modern data technology, aided by AI, has helped furnish RMs with a depth and breadth of information that increases their chances of a successful “pitch”, increasing success rates and boosting advisor productivity in this regard.
Chris Burke, Vice-President, Digital Solutions and Sales Enablement at RBC Wealth Management explained: “Natural Language Processing [NLP] is helping banks to gather heretofore inaccessible insights and relationships extracted from new and significant sources of structured and unstructured client data. There is a growing quantity of information being created about, or on behalf of, clients daily through their engagement with digital and social platforms that allow us to understand the client more broadly, including family and business relationships.
“We don’t see all of that information always. Even though it is freely shared, we are limited in our ability to make the time necessary to find, retrieve, read and process it. This new insight allows us to understand their financial goals more clearly in order to offer more timely and relevant solutions. It also allows us to more quickly find unexpected relationships between world events and our clients, allowing us to be more proactive in our advice-based conversations.”
Looking for “diamonds in the rough”
David Teten, Managing Partner of HOF Capital, thinks this “finding diamonds in the rough” aspect of AI is highly significant. “AI can help mine public data sources to find out, for example, the value of the client’s home or homes, or the value of the company that they have sold,” he said. “AI is helpful in deciding who I should solicit, how I should reach out to them, what language I should use and in preparing the sales collateral that will resonate most with them.”
As Teten observes, AI can prove invaluable in providing “ways in” with prospective clients, helping advisors precisely tailor conversations to build affinities and trust, and so improve their chances of winning a new client.
One application he highlights is how AI can perform scenario analyses enabling an advisor to quickly be able to examine the implications of scenarios – like geopolitical shocks – that might be on a prospective client’s minds.
An even more granular tool he has invested in helps support the sales process by refining - in real time - the “script” the sales person uses to optimise the conversation. “You still have a human carrying out the calls, but they do a better job as they are supported by an electronic ‘coach’,” he said. “You still need the credibility of a human for sales, but the technology helps them effect far greater sales success.”
Peter J Scott, an expert author on AI’s potential, also notes that with so much information available in publicly accessible ways, AI has a particular potency. He observed: "The parallel might be with Google and reference librarians: in a few seconds I can get out of Google what I couldn’t get out of a reference librarian 30 years ago,” he said. “I still really like our librarians; they are friendly and human and they understand me personally, but there are a relatively small number of things I would approach them for compared with what I ask Google now.”
“We will see a lot of things move in that direction. It is only a question of when, not if.”
Surmounting the language barrier
This is a cosmopolitan industry, and facility with foreign languages and sensitivity to cultural differences is clearly crucial for those firms wishing to deliver profitable business. With modern technology moving into areas such as language translation, the potential here in the lead generation and management side looks very interesting indeed.
In the earliest stage of contacting a client, figuring out his or her native language (and which they prefer) is vital, and AI has a part to play here, Tonchia explained: “The issue of ascertaining which language the client speaks is not trivial. A prospect could have a Russian name, own a Russian company and therefore be mentioned in the Russian press, but at the same time they might live in the US and not speak Russian, or at least not by preference.
“Rather than acting blind and hoping a prospective client speaks a particular language, with AI you will be able to attune your strategy to their evidenced preferences.
“With AI technology ‘reading’ all available data sources you can find out things that help foster relationships you never normally could, like a prospect speaking three languages. Then, with AI analytics you can discover what the most popular languages are, mapping your current and target client base to discover that perhaps you need to hire more Russian-speaking advisors.”
As our expert panel highlight, the full gamut of assistance AI can offer on the prospecting front ranges far beyond the obvious. And, while landing new business is still a uniquely human talent, surely few relationship managers will choose to shun the additional support AI can provide – particularly when the pressure on them to gather new assets just keeps ratcheting up.