November 10, 2020 under
USING FINANCIAL DATA RESPONSIBLY TO BUILD TRUST WITH CUSTOMERS
By: Nathan King, Senior Experience Strategist, RAPP SF
Over the past few years, there’s been an explosion in personal financial management apps to help us manage our money across multiple accounts and make better financial decisions. Apps and services like Mint and Personal Capital connect and combine data from different accounts to give us a full view into our finances and give us recommendations on how to reach our goals. But while there has been some innovation in the space, these services have plateaued in their effectiveness.
Consumers face myriad and evolving financial challenges while we see rates of financial literacy and confidence plummet. As these companies have created vast data sets from consumer behavior, a far more ambitious idea has begun to emerge: personal finances managed — and even controlled — by artificial intelligence and machine learning.
Today, most personal financial management, or PFM, services have adopted similar approaches to helping users reach their goals based in behavioral psychology and empowered by CRM mechanics. Acorns was one of the first to “nudge” users into saving by rounding up cents on the dollar of each payment, allowing users to set it and forget it. Digit takes a similar approach but sends daily updates via in-app notifications to give a sense of progress and accomplishment.
But there are three big limitations.
First, PFM services depend on data sets that are difficult to build and manage. The data these third parties are reading from your bank and credit card statements aren’t returned in a normalized fashion. Merchants can appear under hundreds of different names on a statement and must be categorized properly as dining versus grocery, or travel versus entertainment. Further complicating matters, banks don’t return equal amounts of your account history. If algorithms are only as good as the data they’re built on, first parties with a depth of customer history have an advantage.
Second, PFM services are difficult to monetize. Because not many people want to pay for apps designed to save them money, third parties profit by selling your personal data. In some ways, this can benefit customers by connecting them with relevant products and offers. But how, when, and with whom the data is shared isn’t made clear.
Third and most importantly, we’re human — and sometimes the best and most rational plans are no match for our emotional, impulsive brains. Just like the weight-loss app that tells us to avoid bread for a month, spending and savings recommendations are easy to ignore, even when we are confronted with the impact of our repetitive bad habits. The 68 cents you rounded up from your morning coffee is no match for that blowout shopping spree.
The PFM services of today won’t be around forever. But building an intelligent personal financial management app that organizes your entire financial life is no small task.
Set Your Financial Destination and Forget It
Powered by evolving capabilities in machine learning, the vision of autonomous finance has come into clearer view — the goal of which is fully automated financial management. The term “self-driving money” has entered the lexicon, which takes its name from the way you get directions on a mapping application: You tell it where you want to go, you don’t care exactly how you get there, and you trust it to give you the best path while adapting to changing conditions.
The key differences of a self-driving money approach are in the depth of automation and the breadth of reach it has into your financial life. As an example, when your paycheck comes in, it may allocate a percentage to your 401(k) for retirement — some to your kid’s 529 plan, a bit toward your discretionary vacation savings fund, and then provide you a monthly budget across all your typical spending categories. When paying at the grocery store, for instance, it may automatically use the credit card that will maximize your rewards points but use your debit card for most other transactions.
To be sure, the proliferation of personal finance management apps has sparked innovation within banks and tech companies that rely on personal data to enable this level of financial planning. To build the experiences of tomorrow and prepare consumers for what will become available to them, institutions, neobanks, and players of all sizes should consider the following:
Demystify and clarify what data is being collected and how it’s being used to benefit the customer, and give them an easy opportunity to opt out. Companies that lead in this area ahead of future regulation will have a step ahead — and more importantly, will establish a relationship of trust with their customers.
Third-party integrators are stepping in to connect disparate financial accounts and building far-reaching (albeit incomplete) customer profiles in the process. The rich data you already have is your advantage to create personalized experiences that draw them in first.
PFMs are utilities that provide value to the customer and business by connecting them with different financial products as they grow in their financial journeys. But customers are wary of being sold to right off the bat. For example, utilities like Capital One’s Auto Navigator help customers find cars in their area and get pre-approved for loans without checking their credit reports. This provides value in itself and makes an auto loan a natural next step.
“Banking as a service” is allowing digital upstarts to offer banking products without the overhead of actually becoming a bank. As incumbents risk becoming disintermediated, they will win loyalty from the breadth of their offerings and the depth of their customer data that allows them to deliver superior customer experiences.
Granted, true autonomous finance is at least a few years away. To start, most of us don’t currently use one money app, but we have a “money folder” of disparate apps to manage our finances. The first step toward a single experience will be consolidation of these services or broader integration of one service into the next.
In the coming months and years, self-driving money in the United States will probably look more like open banking in the U.K. in the West, than the single platforms of China in the East. But have no doubt, autonomous finance is on its way. Companies like Wealthfront already have plans to use machine learning to reroute leftover money from a customer’s savings into their investment portfolios.
We all make irrational money decisions. The sooner we can learn to surrender some control to intelligent systems and exchange our data in a relationship of trust, the better our aggregate financial outcomes will be.