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Is artificial intelligence the future of financial planning?

InterGen Data was recently featured in proactive advisor magazine. A copy of the original article "Is artificial intelligence the future of financial planning?" by Katie Kuehner-Herbert can be found http://proactiveadvisormagazine.com/is-artificial-intelligence-the-future-of-financial-planning/.

Industries across the spectrum are now finding ways to leverage artificial intelligence to better serve their customers—the financial-planning industry is no exception.

Artificial intelligence (AI) has garnered much the same reaction in the financial-services industry that robo-advisors generated several years ago.

“Oh my gosh, it’s going to disrupt the industry!’” says Doug Fritz, CEO and founder of F2 Strategy, a technology and consulting firm based in Santa Cruz, California. However, while robo-advisors have changed the industry to a certain extent, they have not materially impacted how clients work with their financial advisors.

“AI is going down the same path. People say, ‘Robots are going to take over!’ But that’s just more noise and it distracts people from what they really should be focused on,” Fritz says.

Custodial companies and fintechs are now developing AI and machine-learning tools that advisors can use to more accurately predict when and how clients may need certain advice, freeing up advisors to do what they do best—help clients develop the right plan to meet their lifetime financial goals.

Custodians leveraging AI

The key to using AI and machine learning, the natural-language processing part of artificial intelligence, is to have high-quality data that is readily available and trusted by the advisor and other stakeholders, Fritz says. Without a centralized source of quality data, most of these tools don’t work, he says. But that takes the integration of information from the custodian, the CRM platform, and other platforms that advisors use in their practices.

“There are some custodians that seem excited and energized to help advisors with these types of tools and capabilities,” Fritz says. “I’m impressed with Schwab and Fidelity wanting to break down walls by working to have the right type of architecture and mindset, and to some degree TD Ameritrade.”

The goal is for advisory firms to have their own set of data, emancipated from their technology and custodian partners, in which they can bring together client profile data, account holdings, price history of transactions, planning data, and compliance data, he says.

“Having this all in one place linked together would allow these firms to properly leverage the power of AI and machine-learning tools,” Fritz says.

Pershing is developing algorithms to make sense of the large amount of data across its platform for a wide range of institutions and retail-oriented clients, says Greg O’Gara, a senior research analyst for Aite Group’s Wealth Management practice. The goal is to move from a reactionary business intelligence model for advisors to a more proactive engagement model.

“Custodians are implementing business intelligence analytics for their advisor clients by looking at advisor and investor actions across the platform to determine which advisory firms are the most profitable and why,” O’Gara says. “They can then add value through practice management solutions and digital alerts to advisors who are suboptimal performers.”

The custodians are correlating different variables within RIA and other practice models, benchmarking what advisors are doing within certain asset groups, he says. They are also analyzing client demographics and geographies—a wide array of data points—trying to correlate what makes a successful advisor.

“They are then presenting that information to advisors, perhaps in the same AUM grouping, who are not achieving optimal profitability and probably not providing the most compelling client solutions,” O’Gara says.

Vendors are also integrating client information from custodians and other vendors that advisors connect through APIs (application programming interfaces), he says. For example, the Salesforce CRM platform typically holds robust client data for advisors that can enhance the client-advisor dialogue in a financial-planning conversation.

“The goal is to apply sophisticated algorithms to all of these integrated data sets, to provide advisors with suggestions on how to improve their practice,” O’Gara says. “It will also provide suggestions to advisors on what a client’s next action might be, based on client behavior.”

Currently, voice recognition technology exists in Alexa-type applications, where a client can get answers to questions such as “What is my account balance?” However, adoption is somewhat mitigated by the level of comfort with a given technology. “I don’t think we’re quite there yet,” O’Gara says.

There are also regulatory concerns. Advisor-client communications have to meet compliance standards, and firms need to get more comfortable with digital-channel adoption, though this transformation is starting to happen, he says.

“AI will ultimately help advisors move from reactive actions for their clients to predictive actions, where an advisor may be able to prevent a client from leaving,” O’Gara says. “Additionally, bringing in data aggregation tools, and looking at actions that clients are taking outside of their primary provider, will help advisors to deliver better advice.”

Machine Learning Chart Sources: Venture Scanner, Statista estimates

Wealth Wizards’ “fintegration” approach

Wealth Wizards offers Turo for Advisers, a white-label automated advice platform for financial planners that incorporates both machine learning and AI, says Simon Binney, business development director of the UK-based fintech. Turo has machine-learning capability to discern a planning firm’s particular advice policy and philosophy after examples of client cases are entered into the platform. The solution then uses AI to learn from past cases and makes recommendations for future advice based on algorithmic patterns and outcomes.

Turo does the “heavy lifting” of fact-based data collation, automated quotation feeds, solution mapping, and suitability report generation and leaves the advisor free to focus on customer-facing and relationship-building tasks, Binney says. “We are now able to use our tech to look at the advice our advisor clients are already giving and use this to tailor our tech so that it gives advice that’s relevant and specific to their brand,” he says.

Good advice also needs to be highly ethical and delivered by financial planners with the right qualifications and professional standards, Binney says. Wealth Wizards is mapping these standards into its technology to ensure that these key qualities “are embedded in every piece of advice that is given.”

The digitization of financial services will introduce a better, more comprehensive assessment of customer needs. It will do this holistically via “fintegration,” where multiple solution providers join together to combine solutions to better meet clients’ needs, Binney says. Solutions will need to be fast, highly competitive, able to provide advanced valuation of customers, and able to anticipate their ongoing needs.

“Imagine you’re talking to an advisor who is being prompted with questions on screen,” he says. “The AI listens to your answers and populates a fact find, together with ‘sentiment analysis’ to work out your attitude toward risk.”

AI will also be integrated into the advice itself, Binney says. Financial planners can “teach” it how to advise by giving it previous advice cases that it can analyze. “In the future, using AI, we can make financial advice cheaper, quicker, safer, and more consistent, allowing and facilitating access for more people,” he says.

Redtail Technology: AI learning based on advisors’ data

Redtail Technology in Sacramento, California, now has AI capabilities on its platform to better predict and estimate client needs, create better outcomes, and “discover important connections that enable all parties to make better financial decisions,” says Brian McLaughlin, CEO.

The solution uses machine learning as a launching point to build additional value by clarifying financial planners’ workflows so they can make better decisions, McLaughlin says.

“This is a starting point of AI for financial planners,” he says. “It will continue to evolve, but we need to provide a baseline to get people comfortable with the computer learning on their data.”

Redtail’s solution can help financial planners have better conversations by analyzing the histories of their emails, text messages, advisor notes, and other threads of communication they have had with clients and prospects, McLaughlin says.

“Say a client has mentioned their child will be going to a certain college, but the advisor may have forgotten or overlooked that,” he says. “Our solution presents statements like these as well as sentiments in a more focused way, so the planner can respond more quickly and have more effective goal-based planning conversations with the client.”

Redtail is also interested in looking at how AI affects the integration of planning tools, synchronizing relationships with goal-based and cash-based solutions. “To me, the really interesting evolution of how we use AI is not so much for automated client account maintenance, but for automated goal-based planning,” McLaughlin says. “That will be really cool down the road.”