How to Prepare for AI in Financial Planning

Journal of Financial Planning: January 2019



Let’s face it: advanced artificial intelligence—like machine learning and deep learning—are not yet scalable and feasible to roll out broadly in our industry’s regulated spaces. Yet, most of us understand the impact that changing demographics and shifting expectations will have for those planners who leverage scaled technology.

The math is simple—more clients and fewer advisers and planners, plus increased client expectations of services and decreased willingness to pay for asset management. Advisers and planners need to broaden their services and have meaningful engagements with their clients at increasing frequencies.

So, how can you think about adopting some of this now and prepare for the later phases of AI? And, how can you get your practice and clients ready to take advantage of this impending technology shift?

In looking at artificial intelligence more closely, I’ve observed four phases to the technology: (1) rules-based; (2) simple machine learning; (3) deep learning; and (4) adaptive learning.

Rules-Based Tools

Let’s start with some simple rules-based tools. The first step is improving your planning tool. This means aggregation. Meaningful data to feed an engine and train the engine is key. It can also deliver real value today to the planner.

Traditional planning completely ignores clients’ daily spending, and often, clients are not fully transparent with their spending, savings, and investments. The impact of rules-based tools broadly deployed to your client base and made available to your prospects can directly improve their daily transaction lives by improving their decision-making process and awareness—and it takes almost no time.

One of the main reasons clients can be slow to aggregate is they do not get value in return. The value to date has largely been to make the planner’s life easier. To drive adoption, aggregation needs to be about the client, and we need artificial intelligence to help scale insight and increase the value of that data.

Machine Learning

Machine learning takes those daily transactions and recognizes patterns to start making intelligent projections. Put simply, once you have the data, AI can help you grow wallet share and can help your clients make better decisions, which can increase their savings rate and improve their outcomes.

However, just data collection with applied AI to measure and evaluate behaviors is not enough. We need to improve how we communicate with our clients and prospects. Adding to this challenge, the day is still only 24 hours long, yet the number of clients we serve continues to grow and the number of services we’re expected to deliver is ever-expanding.

The next big technology impact in our space is natural language generation, which commonly leverages machine learning and some deep learning. The financial services industry needs to do a better job translating complicated data into accessible stories.

Too often I see clients receiving 40-plus page proposals and even longer financial plans. Higher page counts are often unfortunately misinterpreted as value by some planners and clients. But we know we don’t read all those pages and clients don’t either. This practice is a one-time event, and the value in the exercise is lost if we can’t communicate it timely and succinctly.

I often hear from advisers that their job is to be the interpreter of charts and graphs. While that does provide value and gives you a reason for meeting with clients, it’s not scalable. Let natural language generation (NLG) do a lot of the heavy work. Firms like Narrative Science are transforming data into plain English stories.

AI and NLG can help planners deliver personalized narratives to clients anytime and on any device. These narratives, if built well, can easily link the client’s holistic picture of investments, planning, and daily transactions, and give them a simple summary that explains where they are currently, where they are going, and how they can do better. I believe the question of “how they can do better” is where these new technology services can be the most impactful.

Using artificial intelligence to deliver behavioral nudges to clients is a great way to help scale your coaching and planning. You know what’s better than 20 beautiful, engaging charts? One simple paragraph that says what is important for your client to know today.

AI Chatbots

One of the biggest ways AI is used today across different institutions is AI chatbots (think Siri and Alexa). There are many stories of these chatbots going horribly wrong at the largest, best-funded, and smartest firms out there, such as Google and Microsoft. However, I’m still extremely excited to see chatbots get to the point where they can reliably interact with financial planning and advisory clients.

Today, AI chatbots are best suited for simple, transactional tasks, but in the next few years, we will see them start doing some basic coaching and planning. For advisers and planners, this means a continued shift to new services, adopting ​technology to help you do the tasks you spend time on today, and looking forward to the day when you don’t have to explain a Monte Carlo simulation.

Adaptive Learning

Looking forward to more adaptive AI opportunities lets us envision an advisory practice 10 years down the road. Advisers and planners will need to operate more like a family office to continue to be perceived as relevant and highly valuable. If you are not already, you will need to be engaged in finding clients the best access to credit and insurance, for example. It could be as simple as a decision to lease versus buy a car, or a long-term health care insurance plan evaluation.

Data and AI can be applied to bring down the cost and increase the speed and transparency to which loans can be made, resulting in more choices. More choices are something consumers don’t always do well with, and clients will need guidance. A relationship with a strong foundation of trust and data will drive toward the best outcomes for clients.

Speaking of outcomes, the most important outcome is a client’s health. It will be interesting to see how we are ultimately able to combine health data with financial data. I predict that we will start seeing more combinations of firms that serve both financial wellness and physical well-being. We will be able to better track and manage future health care needs. This is where the deep learning practice within artificial intelligence will really be able to provide us with incredible insights and without a doubt, will change the way we plan. Even more importantly, it will change the way we live.

Blake Wood is senior vice president and director of product strategy at Envestnet. He spends most his time outside the office traveling to meet with advisers and financial institutions to understand the broad set of problems facing firms today.