Applying Artificial Intelligence in Wealth Management: Compelling Use Cases Across the Client Life Cycle. This article is part of a series about AI and its use in the industry.

Tom Burroughes, Group Editor at WealthBriefing, examines how Artificial Intelligence can be used to hone holistic financial plans and customise portfolios in order to optimise both client outcomes and firms’ operational efficiency. (Click here for link to details about the report, including a registration field.)

In today’s complex wealth management arena, when advisors want to know how best to put compliance rules into practice, or figure out what might be the most suitable jurisdiction for a trust, Artificial Intelligence has the power to give managers an added edge.

An advisor will want to know how AI can help extract data from clients more easily and ensure financial plans and investment portfolios meet their needs as accurately as possible. And these objectives are becoming more varied: we have moved a long way from simply figuring out if there will be enough money in portfolios to pay for retirement, school fees or a house. For example, research shows investors increasingly want their money to do good things in the world, as seen by the trend of environmental, social and governance-related (ESG), responsible or impact investing. Such goals involve pulling a lot of information together fast, which is one area where AI can be particularly effective. Then, there are the tasks of serving cross-border clients who speak various languages and where local regulatory, tax and other considerations come into play.

There is already a sizeable body of opinion that sees real value in AI’s fit with investment management. 43 per cent of wealth management professionals globally see the technology offering great potential to improve investment performance and risk management.

And one takeaway from conversations with industry practitioners is that AI isn’t something that is still in the laboratory. It is being used right now. 

Phil Tattersall, Director in EY’s UK Wealth & Asset Management Data and Analytics advisory practice, believes we should expect big changes in how investments are managed because of AI.

He said: “Undoubtedly, AI and ML [Machine Learning] technologies will be key to revolutionising the investment process. In fact, right now 62 per cent of systematic hedge fund managers are using machine learning techniques in their investment process. 

“The driver is the increasing digitisation of everything - more and more data being generated by humans, such as through social media, as a side-effect of business processes, and by sensors.

“All of these data sources can be considered alternative data that can generate new insights for the investment process. However, making sense of these complex, variable, voluminous alternative data sources is tricky and requires sophisticated AI/ML techniques to help portfolio managers spot patterns that humans can’t easily identify or couldn’t spot at all in the deluge of information.”

Human psychology is not a closed book to AI and this holds great potential when it comes to managing money, argues Peter J Scott, an expert author on this subject. “We see a lot of studies now that have been turned into marketable books telling us about the psychology of people and how they can be relied upon to behave in certain ways that contradict rationality but we nevertheless do all the time,” he said.
“For example, humans can have asymmetric risk preferences in how they consider the future, and AI can avoid some of these biases, helping them make wiser investment decisions as a result.”

Importantly, AI can be very powerful in assisting money managers and clients make sense of data, argued Alessandro Tonchia, Co-Founder of Finantix: “AI can help you interpret a performance report, enrich it with information that explains the relevant market trends and add a lot of value to your discussion,” he said. “You can really bring the information to life and enable meaningful dialogues with clients.”

Chris Burke, Vice-President of Digital Solutions and Sales at RBC Wealth Management, said he sees AI as bringing more precision in terms of the kind of information at managers’ fingertips, along with far more speed. It can also help foster the absolute client-centricity consumers have come to expect. 

“Put simply, advancements in AI allow us to leverage all of the data generated within our client and prospect relationships to deliver more targeted and personalised digital experiences, as well as more targeted guidance for our advisors and front-line staff,” he said. “Rather than requiring the client to navigate a complex organisation based on our own structure and taxonomy, we can now present highly-relevant insights and content in real time.”

Scenario planning
In working out what works best for clients in their portfolios, an important idea is around how to generate lots of different scenarios that a person might find themselves in, and showing how they will need to act. This can sometimes be referred to as “gamification” - borrowing from the language of computer games - or simulations to illustrate different scenarios for clients. Testing out different scenarios can also be a powerful teaching tool in learning how to fine-tune portfolios. 

EY’s Tattersall expects robo-driven investment to continue gathering momentum, giving the following reasoning: 

“’Freestyle’ chess allows humans unrestricted use of computers during games. This combination of humans plus computers still beats the most powerful computer by itself. In a similar way, despite the fact that the power of the algorithms automating advice will continue to increase, we should expect that for the foreseeable future humans plus powerful algorithms beat algorithms by themselves.”

“While we can expect the penetration of robo-advice to continue to accelerate, we should expect that the ultra-high net worth segment will continue to be best served by their trusted human advisor but augmented with ever more powerful algorithms,” he said.

Finantix’s Tonchia said AI can be especially useful in generating particular outcomes depending on specific client situations, covering not just investments but more complex advisory areas that can include tax and estate planning as well.

What is more, in some cases, rules and principles can be generated not by advisors and firms, but by the AI tool itself.

“In certain contexts, we don’t need to code rules because they can be machine-learned by abstracting positive experiences: the technology takes what works and what doesn’t work in your organisation for certain clients and distils the rules automatically,” said Tonchia.

“In that case, you don’t need expertise so much as you have to set the model up to learn from experience; in other more complex cases, you might need to have financial planning or estate planning experts define those rules and enter them into the reasoning engine. A hybrid approach is also possible.”

Cross-border compliance
One increasingly important issue for wealth managers is understanding when an investment that might be deemed suitable in one country may not satisfy standards in another. Keeping on top of a patchwork of regulatory tests and requirements will tax the cleverest manager, which is where AI can help hugely to reduce risk and increase efficiency.

For firms considering selling products and services into different countries, AI can remove the chore of managers having to constantly consult their compliance departments to ensure they aren’t breaking the rules, Tonchia explained. “There will still be a need for traditional compliance for the hard cases, but 90 per cent of the workload is just routine questions like ‘Can I sell this product to this client?’,” he said.

“That’s something that you don’t want to bug the compliance guy with every ten minutes – or need to with our technology.”

If AI can perform the heavy lifting of finding out which products and services are suitable, this frees up advisors to do what humans do best – giving overall strategic guidance to clients, which is also where they can really add value, Tonchia continued. 

Adding other technology to AI
Another facet to all these issues is how AI might be combined with some of the other hot developments in fintech to even more powerful effect.

“Blockchain and Distributed Ledger Technologies provide decentralised/shared control which encourages data sharing and enables new data to be made available, both from within a wealth firm and from across its wider ecosystem,” EY’s Tattersall said. “Recently, LendingRobot launched a hedge fund that uses machine learning techniques to select alternative lending opportunities (such as peer-to-peer loans) that best match the investment objectives while providing complete transparency into the immutable ledger of investor and investment activities that is held on the Ethereum blockchain.” 

So, will the combination of AI with other fintech areas make what is called “holistic” wealth management a reality?

According to Dr Anthony Kirby, Associate Partner, Regulatory and Risk Management - Regulatory Intelligence at EY in the UK, his firm certainly sees AI being usefully blended with other tech to create “consensus approaches” that draw all parties to the investment process, even extending to providers of professional services such as regulators, asset managers and accountants.

“For example, the UK’s Financial Conduct Authority announced in mid-September 2017 that it had developed a regtech application for the mortgage market in collaboration with at least two leading banks and the blockchain consortium R3 – the latter featuring more than 80 members, including banks, clearing houses, exchanges, market infrastructure providers, asset managers, central banks, regulators and professional services firms,” he said. 

So, it seems that AI is already poised to help wealth managers deliver on what are arguably the most important parts of their value proposition, creating the ability to precisely customise investment portfolios and financial plans cost-effectively and with reduced risk.