Businesses generate massive amounts of data, but as the amount of data grows it becomes harder to analyze. Excel spreadsheets,Kanban boards, and CRMs are all useful for basic analysis and organization. They are, however, much less useful for advanced analytics, such as client behavior, risk assessment, and individualized recommendations.
These deeper insights can be found in the data that firms already have, and artificial intelligence and machine learning (AI/ML) is perfect for this task. AI/ML can process and analyze data far faster than any human can and works well in finding patterns in the data that manual analysis misses.
AI/ML technology is now used across nearly every industry to varying degrees – including finance – where demand for AI/ML is high. Accenture found that 87 percent of financial advisors surveyed would use more AI/ML tools as part of their day-to-day work if given a chance, and that interest remains even if there’s training involved.
Despite such high interest, finance lags all other industries in utilizing artificial intelligence, with few options for financial professionals such as Registered Investment Advisers and Registered Representatives to incorporate the technology into their processes. A 2021 Accenture study found that the financial services industry ranks last in terms of AI maturity, which is defined as “the degree to which organizations have mastered AI-related capabilities in the right combination to achieve high performance for customers, shareholders and employees.
Some analysts argue that true AI within the financial services industry all but doesn’t exist, saying what many financial professionals think “AI” is merely automation. While this argument is an exaggeration of the state of AI in finance, there is some truth to the criticism, as many tools focus on speeding up time-consuming tasks rather than providing actionable analysis.
So where can true AI/ML revolutionize the advisor/client relationship and allow firms to harness and utilize the massive amounts of data they likely already have?
One of the companies at the forefront of bringing machine learning-based tools to the financial services industry is Softlab360. CEO Henry Zelikovsky sat down with us to discuss his company’s predictive analytics efforts and how Softlab360 is working to close the AI maturity gap in financial services.
Here are three ways AI can add value for financial advisors and, in tandem, the financial services institutions that support them:
- AI enables more insightful analysis
- AI can help guide client conversations
- AI can help demystify crypto
AI Enables More Insightful Analysis
Conversion and churn are two of the most commonly used measures of gauging a wealth management firm’s success in winning and building long-term relationships with its customers.
CRMs have long provided some level of predictive analysis of conversion and churn. However, these methods use generic measurements and simple mathematical formulas (i.e., the client’s position in a sales pipeline, the time elapsed since the last contact, the percentage of clients lost to competitors, and so on).
These are, no doubt, helpful in a general sense. However, these measurements provide little benefit to understanding why clients behave the way they do, nor can they accurately predict future client behavior. That requires more advanced data analysis than is possible with a CRM alone.
Softlab360 believes the answer lies in machine learning. The company already has a proof of concept, having built a system to help advisors who custodied client assets on the TD Ameritrade institutional platform recommend products based on their client’s trading patterns. Zelikovsky believes a similar solution could work for any wealth management firm.
The Softlab360 platform uses historical data to predict potential life events that a client may encounter and then recommend strategies to prepare financially for those events, whether good or bad.
“With AI, you know your customer, your expectations as an advisor, and what you need to plan for versus what’s happening right now,” Zelikovsky said. “Machine learning can also provide impartial analysis of the next steps and potential strategies to meet clients’ goals that don’t steer them to particular strategies and investments.”
AI Can Help Guide Client Conversations
Zelikovsky sees significant value in using AI/ML to help advisors guide clients through potential future life events that have a high probability of occurring during their lifetime. It can also provide valuable insight into what works and what doesn’t for a specific type of client.
In the past, advisor experience, education, and training played a significant role in what an advisor recommended. By pooling the collective experience of all advisors through machine learning-enhanced tools, relying solely on the strength of any single individual is less of a factor.
AI/ML can suggest relevant conversation topics based on various factors. It can also help advisors determine when to discuss more complex and sensitive issues with their clients, such as retirement planning and end-of-life care.
Think of it this way: What an advisor should discuss with a 40-year-old client with a history of good health and long life in their family will be significantly different than the discussion with a 25-year-old looking to start a family but with a higher-than-normal risk of poor health later in life.
“Advisors need to point out the negative life events (diseases, heart attacks, cancer, etc.) that exist in your family history, because they might happen,” Zelikovsky said. “But more than just a negative sense. You want to guard against it because we’re living longer as healthcare improves.”
Guiding the client through the good and bad will engender trust, which is critical for a long-term advisor/client relationship, the latter something that Zelikovsky stressed throughout the interview.
“As an advisor, I can look at these scenarios and anticipate what may happen to my household and receive tips on how to raise the issue during the conversation,” he said. Softlab360’s solution doesn’t tell the advisor what to say; they can choose how to present these recommendations.
“That’s part of an advisor’s skill set, right?” Zelikovsky asked rhetorically. “You’re trying to maintain a level of conversation that gives people a reason to trust you and want you to manage their money.”
AI Can Help Demystify Crypto
Despite the “crypto winter,” that the market is currently experiencing, digital assets remain extraordinarily popular and show no signs of disappearing anytime soon. As a result, advisors should expect clients to inquire about crypto investing and whether now may be a good time to enter the market, especially as it appears the market might be finally finding a bottom.
AI / ML may help advisors better assist crypto-curious clients, especially as firms like Softlab360 gather the data necessary to help recommend the digital assets that make the most sense for the client’s investment strategy and goals.
Softlab360 is working with digital asset platform FalconX to develop new crypto tools. Initially, these are limited to risk analytics to assist advisors in understanding how adding crypto assets will impact a client’s portfolio.
While these tools are basic at the moment, Zelikovsky said Softlab360 is focused on advisors’ current needs rather than overwhelming them with functionality and tools that won’t be immediately useful.
Working with FalconX gives Softlab360 the data necessary to build analytical tools to help advisors effectively recommend digital assets to their clients. “We have not done this work yet in an analytical sense, but we’re working on getting there,” Zelikovsky said.
An Experienced Software Engineering Team Can Help
As we noted at the beginning, Accenture found advisors want to incorporate AI/ML into their daily workflows, even if it involves training. But it also found that many were skeptical of their firm’s ability to successfully integrate the technology in a way that would be useful and are overloaded by too many “AI” tools that provide little value.
Half of all respondents said their firms were “challenged” to adopt AI, with slightly more than half saying that their current AI tools were too difficult to use. But perhaps the most critical finding is that two-thirds of all respondents said their firm was taking on too many AI pilots in their efforts to adopt artificial intelligence and machine learning.