Artificial intelligence is sparking new waves of innovation, revolutionizing not only how we work but also the products and services we offer. While that may sound ambitious, as the global head of technology and product at Morningstar Retirement, I’m tasked with exploring ways to leverage AI and other new technologies to better our retirement planning services.
AI is already making our development process more agile. We are using these tools to rapidly prototype and test new concepts, helping us validate ideas faster and refine them into better user experiences. Even in my personal life, I have used AI to turn my creative vision into reality.
A heartwarming example being when my ten-year-old son came to me after learning what I do for work and expressed an interest in investing. As a father, I want to share this part of my world with him, but how can I break down a system as complicated as investing into terms a child can understand? Knowing he loves a comic book series called Big Nate, I turned to generative AI to bridge our worlds. Using an AI image and text generator, I wrote a Big Nate-inspired comic that explained the basics of investing through middle school hijinks. While my son is still a ways away from a Ph.D. in economics, he was inspired enough to ask me to open a brokerage account for him on his birthday.
In short, we used AI—under my careful guidance—to teach complicated financial terms and strategies to an unsophisticated investor in an engaging way. Now imagine if we were able to do that for all savers?
Our flagship service, managed accounts, was designed to give the everyday saver the type of personalized guidance typically reserved for working directly with an advisor. Now, with AI’s ability to process more complicated datasets and generate unique content, I believe we can get managed accounts even closer to replicating the comprehensive and intimate advice of an advisor.
Humanizing the Robo-Advisor
As it currently stands, managed accounts is one of the most tailored-to-the-participant retirement planning services, second only to working with an advisor. The service actively managed participants’ investments while also providing them with dynamic guidance on how much to save, when to retire, and when to take Social Security. While we already personalize this advice using a host of factors like lifestyle, income, and location, AI could empower us to take even more participant information into account.
For example, the retirement industry currently revolves around saving—how much to save, where to invest, and when to withdraw. Yet, there remains a large blind spot: daily finance.
It can be frustrating for participants to be asked to save more when they are already struggling with their existing budget. However, with AI’s ability to parse through complex datasets, we could assess their expenses to identify potential areas where they could reallocate funds to make room for their retirement. We could even go a step further and create adaptive contribution rates that ebb and flow each pay period based on the participant’s unallocated dollars. Moreover, understanding a participant’s spending habits could allow us to come up with more realistic retirement goals. Two people making $100,000 a year could require radically different sums of money if one has more necessary expenses than the other, like medical costs or caring for a family member. By knowing how much they actually spend, we can tailor our advice to better reflect their retirement income needs and help them fit retirement saving into their budget.
Another avenue AI can assist with is addressing knowledge gaps that may prevent some savers from making educated decisions with our advice. Just like how I had to adjust my investing lesson for my son, many savers could benefit from personalized educational materials that speak to their specific situation. Since AI can create these materials at lightning speed, we could customize content not only to fit the person’s education level, interests, and finances, but also serve this information to them in their preferred medium: chatbots, videos, tool tips, or micro lessons. By adding in these guiding features, managed accounts could more closely resemble the care advisors can provide their one-on-one clients and may inspire savers to be more invested in their retirement journeys.
Enriching Client Relationships
The benefits of AI extend to the advisor who offers the managed accounts service by allowing them to customize the enhanced service with their retirement philosophies. Furthermore, it can empower them to form closer connections with their clients by driving engagement on both a participant and plan level.
On the participant side, predictive analytics can identify when participants are most likely to take action. By knowing when participants are ready to make decisions, advisors can nudge them to return to the platform to update their retirement strategy. Moreover, the advisor can personalize these nudges to the participants’ interests and circumstances for a more compelling call to action. By making the participant feel more “seen,” we hope that they become more involved in their retirement planning.
On the plan side, AI can help demonstrate to plan sponsors how the service is meaningfully improving the retirement outcomes of their participants. For example, we can use AI to generate custom plan briefs that quickly pinpoint the service’s efficacy by highlighting the impact it has had on participants. This added touchpoint goes a long way in improving retention and client satisfaction with little work on the advisor’s end.
Going beyond communication, AI could also help advisors manage their books of business by identifying plans that may require attention. With AI generated custom assessments, we could not only surface at-risk plans but also provide suggestions aimed at rectifying the concern. For example, AI could flag funds that are consistently underperforming, identify which plans include it in their lineup, and offer alternative funds that could replace it. By identifying plans that require extra attention, advisors are better positioned to course correct and keep their plans on track.
Putting Savers First
While AI holds enormous promise, this is still nascent technology that needs further advancement. As a managed accounts provider, we shoulder the weight of our participants’ futures and take that responsibility seriously. Before we can fully embrace AI, we need to meticulously test and iron out any “AI slop” or hallucinations that could negatively affect our advice. Once AI has reached that level, we also owe it to our participants to be transparent about how and where we are using it so that they can make an informed choice as to whether they would like to use these enhancements or not.
Until that time comes, we are leveraging more proven methods to move toward these goals. For example, with better aggregation technology, we can pull more information about the participant (e.g., payroll and spending data) into our existing advice engines to add another dimension to our guidance. We have also added extra automation tools to improve the quality of our advice, such as our continuous assurance program that independently scours every transaction to flag potential anomalies for human review. This extra layer of technology enables us to identify bugs or errors before they can negatively affect participants’ performance. These “smart” technologies are stepping stones to integrating more advanced AI features into managed accounts.
It is my hope that we continue to make strides in our technological infrastructure so that by the time my son enters the workforce, we can teach him the intricacies of retirement planning in the voice of Big Nate.
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