PwC reports that wealth managers will soon be able to guide customers on the financial decisions that are most appropriate to meet their financial goals based on a synthesis of artificial intelligence (AI) and contextual data. The Big Four company predicts that operational costs will decrease dramatically, with emerging business models and modern technologies serving to streamline onboarding processes, operations, and client communication. To date, at our WealthTech Club we have spoken to a number of industry experts and gained an insight into their perspective on the next frontier of WealthTech, and the role AI and big data will play on the way to it.
Algorithmic future of software
Boris Khazin, CEO at Algo-Lead, strongly believes in an algorithmic future. He mentioned that AI will soon be making decisions for investors that are in their best interests. Such an upgrade in technology will benefit the system overall.
“If you develop the software with all the rules and regulations, it will act in a fiduciary manner. For a computer, it’s a lot easier to do that than it is for a human being, since human beings are flawed. And technology will make it cheaper and simpler for everybody to do what they’re doing without human advisors,” Borys Khazin, Algo-Lead
Boris is confident that the need for human customer service representatives will be reduced to a bare minimum, because AI and machine learning will be able to address the majority of queries customers have. Additionally, blockchain adoption will reduce the whole onboarding process from days to minutes, because requests for information and all exchanges will be completed in no time:
“The future is in more algorithmic computer decisions, in more blockchain, instantaneous communications, and onboarding,” Borys Khazin, Algo-Lead
According to Boris, the information wealth managers will typically share with clients will already be stored in a distributed ledger and further channeled when necessary, making the turnaround time more efficient and simplified. When algorithms do the job of a human consultant, retail consumers will have access to the high-level performance that is currently only available to high-net-worth clients.
Florian Spiegl, co-founder and COO of Hong Kong-based FinFabrik, stated that the company uses “full-stack AI” in wealth management, including different layers that all are powered with a different kind of AI.
For example, the client-profiling process is about learning who a potential client is. Wealth managers need to understand the prospect’s past and present situation, as well as their long-term aspirations and goals for the future. The prospect’s credit score is just one part of the equation. Florian mentioned that insight’s from Chinese company Tencent’s payment system, WeBank, could come handy when assessing clients’ creditworthiness. If only a prospect allows wealth management firms access to that type of historical record, this information becomes an additional point to consider when predicting how the investment journey of the prospect will unfold.
It is very likely that in the future we will see more and more WealthTech players offering AI-powered components in their products to streamline certain client-facing processes, and pumping up the margins due to cost reductions pertaining to expensive human capital. Big data will enable companies more accurately tailor portfolios, taking into account not only what potential clients actually declare in standard risk assessment questionnaires, but also social media insights and other types of data at hand.
Furthermore, Sid Sharma, Tech Guru (CTO), CFA, and FRM at Hedgeable, told us how they apply an AI module that focuses on customer experience, investing strategies, and predictive analytics. It seems that Hedgeable is a pioneer in applying AI to the entire digital wealth-management value chain.
According to the company’s CTO, Hedgeable embraces AI in dealing with the following:
- Customer experience: Hedgeable believes that chatbots and smart website search functionalities offer superior customer experience. Sid mentioned that simplicity is the key here, and potential client should not be expected to go through all the explanations in white papers to understand the current offerings in depth.
- Investing strategies are shaped by the usage of machine learning to process large amounts of information—including global macro insights, raw data—and infusing the outcomes into advice on actual investing strategies.
- Predictive analytics helps Hedgeable to understand clients and work in sync with their behavior to create more custom-tailored products and provide smooth customer service.
Hedgeable has tailored its offerings to all types of clients: the company has a broad range of accounts that no other digital wealth-management platform can boast:
“We have appealed across the board for somebody who’s willing to come online, search for a good product, and invest online,” Sid Sharma, Hedgeable
Beyond that, John Logan, Founder, CEO, and Chairman of SafeGuard Guaranty Corporation, thinks that in the coming years more people will shift to software and algorithms that will assess their strength and weaknesses in the market, asking them myriad questions on what they want to accomplish via wealth or retirement planning. After the initial assessment, the financial planner will present them with a list of offerings, with certain kinds of investments that make the most sense and are expected to be beneficial in each specific case. At the same time, a large pool of people will expect a person, alongside the software platforms at hand, to be able to look at what they’re doing and guide them through the customer journey.
Although John does not think that AI-driven solutions will replace the need for human consultants, he stated that within the next five years we’ll probably see a software platform similar to DeepQA of IBM Watson. This will replace the need for call center operators and traditional customer service, but the question remains as to whether this scenario will suit the aspirations of investors who require more complex advice:
“If somebody applies that kind of AI to a total wealth-advisory scenario, it may, in fact, end up replacing wealth advisors and financial planners for people who don’t need to have very, very sophisticated return plans,” John Logan, SafeGuard Guaranty Corporation
As for the blockchain application in wealth management, John foresees this technology to be more beneficial on the B2B side. The main reason for it to skip the B2C segment pertains to its current stage of immaturity. Distributed ledger technology and its application areas in WealthTech are still working in progress, but for firms it can bring down transaction fees and errors in terms of where data is or isn’t, and when sending money back and forth and getting things done.
Big data is going to think instead of a customer
Lex Sokolin, Global Director of FinTech Strategy at Autonomous Research, provided a great example of how big data can be leveraged to benefit investors. Lex mentioned that for real adrenaline seekers who want the best of both worlds—for instance, to evenly split their money between equity and cryptocurrencies—the right investment strategy could be a challenge if they have young children and a family to take care of, for example. This is a situation in which big data can come into play to help the investor understand who he or she is, and what level of risk is appropriate to guarantee a smooth investment journey.
When it comes to financial decisions, people are not really willing to do their homework and dig deep into complex matters:
“That level of sophistication—of using much more complex analysis than a questionnaire on a scale of one to ten (e.g., you have three questions and then you assign somebody […] one out of five portfolios)—making it more complex than that can have the effect of losing your customer because they won’t understand what’s going on,” Lex Sokolin, Autonomous Research
If wealth managers can use big data to remove the guesswork required of humans in diagnosing themselves, they can offer solutions that are more specific and custom-tailored.
“We’re coming into a world where software knows us better than we know ourselves, not in terms of consciousness but in terms of statistical inference,” Lex Sokolin, Autonomous Research
Elaborating further on use cases of big data, Jeff Marsden, Chief Product and Strategy Officer at Xtiva Financial Systems, had a lot to say during our discussion as to whether more data should be used to customize services. Jeff mentioned that with big data vast amounts of incremental information will pour into the process, taking the quality of risk assessment and management to the next level. Jeff is confident that this technology will guarantee an improved ability to predict the risk of something happening that, due to human nature, typical wealth-management consultants are likely to underestimate.
To add even more efficiency to wealth-management software, Jeff stated a few important use cases of distributed ledger technology that are likely to benefit the wealth-management industry:
- Private data with sensitive information being securely stored and shared with financial organizations or individuals, and potentially validated by third parties (in terms of government identification, for example). Another issue blockchain can tap into is compliance applications and customer onboarding processes to benefit financial planning capabilities at scale.
- Operational efficiency for distributed ledger can significantly reduce that reconciliation time and become a huge profit lever and cost-reduction mechanism for brokers:
“The amount of work and time that goes into reconciliations in the custodial and asset-management world is massive, it’s a huge cost,” Jeff Marsden, Xtiva Financial Systems
Jeff mentioned that some major players in the banking industry are already using this powerful technology by, for instance, testing it for conducting internal settlements between business units, because they can have a sole control over the environment.
In addition, Andrei Cherny, CEO of Aspiration, knows how to make a difference and use innovative technology at the same time. The executive revealed that the company uses all kinds of data for things like creating its Aspiration Impact Measurement. This index takes into account more than 75,000 data points and gives clients a better idea of their influence on society when they’re making spending decisions. Clients can see how the companies to which they chose to allocate their capital relate to social good and where they’re spending the dollars when it comes to either environmental issues or work ethics.
“Leveraging huge amounts of data from multiple streams to be able to make those kinds of decisions is essential for us,” Andrei Cherny, Aspiration
But that’s not all. Bob Cortright, CEO of DriveWealth, mentioned that the company is able to offer cutting-edge solutions with a pinch of AI—for example, through its customized portfolios that are tailored to the needs of clients.
Bob’s colleague, Harry Temkin, CIO at DriveWealth, stated that modern technology is built to easily integrate with a number of partners to aggregate the data supplied by multiple parties. Thus, this type of intelligence goes beyond just a standard set of questions, actually bringing in other data factors that can help to create a fairly sophisticated, diversified portfolio.
On the European front, Martin Polasek, Сo-founder and CTO of Swiss company Evolute, noted that a great deal of research in the space of behavior finance has tried to come up with alternatives to assess clients’ risk tolerance. While there is a simple and proven approach to estimate the risk ability of potential investors, the details can be quite complex. In general, the task is to collect all the assets and liabilities of a client, put the findings into a balance sheet, and see how many free assets the client has. This clearly determines his or her ability to take risk without running into financial liquidity issues.
“When it comes to risk tolerance, another component of the risk-assessment process, behavioral finance, suggests that the rich are unlikely to take large risks as the stakes are too high, so HNWIs have lower risk tolerance than risk ability,” Martin Polasek, Evolute
Then again, Greg Vigrass, President of Folio Institutional, explained how machine learning and predictive analytics fit into the company’s offering.
“… we’re building a machine-learning algorithm that we want to use to set the correct risk parameters for customers,” Greg Vigrass, Folio Institutional
With predictive analytics, it’s easier to figure out the person’s risk profile because there is a finite number of questions that one can ask. Machine learning goes beyond the traditional set of questions to understand what it will take to make the customer satisfied, and what their reaction to certain things could be.
Anton Honikman, CEO of MyVest, stated that behavioral profiling has all sorts of other implications beyond just which investment solution the client gets mapped to. It should also impact how the advisor interacts with their customer, the frequency with which they collaborate, and how responsive their interactions are to changing circumstances in their portfolio or market climate. However, Anton added that there’s not yet enough industry experience of this kind of solution.
Shawn Brayman, CEO of Ontario-based PlanPlus Inc., has done some work with IBM Watson APIs on personality insights. Additionally, the company has several research projects underway with at least one university to look for correlations or relationships between big data and behavior (e.g., via psychometric risk tests). Shawn stated that big data is a powerful instrument when it comes to marketing efforts:
“If I look at a million of people and see a certain trend, I can highlight 5% more people that will buy my product. This means I’m targeting a million people,” Shawn Brayman, PlanPlus
The bottom line is that it is very likely that in the future we will see more and more WealthTech players offering AI-powered components in their products to streamline certain client-facing processes and pump up the margins due to cost reductions pertaining to expensive human capital. Big data will enable companies to be more accurate when tailoring portfolios, taking into account not only what potential clients actually declare in standard risk-assessment questionnaires, but also social media insights and other types of data.
