Research > The Ultimate Guide to Data, AI, and Personalized Financial Automation
Voice assistants, chatbots, and personalized financial automation. Regardless of the specifics, today’s digital banking innovations all rely on a foundation of connectivity and data.
Voice assistants, chatbots, and personalized financial automation. Regardless of the specifics, today’s digital banking innovations all rely on a foundation of connectivity and data. Without that foundation, the latest digital initiatives at financial services companies will offer less meaningful experiences.
To illustrate, imagine interacting with a voice assistant that pulls from unclean and unstructured data. You ask the assistant what your last bank transaction was, only to hear a string of random letters and numbers such as, “CSI-308613/22120-CHV.” The correct data is simply “Chevron Gas Station,” but because this particular voice assistance technology is built on a foundation of unstructured and uncleansed data, it’s as good as useless and results in a bad experience.
Or imagine trying to make offers to a customer without the ability to see all their financial accounts in one place. If you only see a tiny fraction of a customers’ data, you may show them an offer for a mortgage loan at a higher rate than the mortgage they already have. Even worse (and not unheard of), you might mistakenly show them an offer for a mortgage when they already have a mortgage with you, leaving them feeling like you don’t care.
The truth is that every digital initiative hinges on the quality and breadth of the data you’re able to access.
To fully understand the state of banking data and AI-driven initiatives, such as personalized financial automation, we surveyed leaders at a range of banks and credit unions and have included the results in this report. By knowing the state of the industry, you’ll better understand how you can become a leader on this front. With the right strategy, the right technology, and the right partners, it’s easier than you might think.
Industry leaders know that data, AI, and personalized financial automation are important, but they often struggle to lead on this front.
2% of respondents say they’re leading the way when it comes to data initiatives, indicating that the competition on the cutting edge is slim.
Taken together, this research reveals an industry that can see the importance of data, AI, and personalized financial automation, but is still struggling to find the right solution to the problem.
This guide will help you get there.
About the Sponsor
MX, the leading data platform helping organizations harmonize the money experience, is built on the belief that financial data should be accessible and actionable for all. Founded in 2010, MX is one of the fastest growing fintech innovators, powering more than 2,000 financial institutions and 43 of the top 50 digital banking providers to improve the financial lives of more than 30 million people. To learn more, visit www.mx.com.
See the MX ultimate guide series, which includes original research data, at www.mx.com/ultimate-guides/.
To better understand the state of data and AI-driven financial guidance in the financial industry, we surveyed a range of industry leaders. Altogether, 56% of those surveyed work at banks and 40% work at credit unions. In addition, these organizations have assets ranging from less than $500 million (24%) to more than $10 billion (17%).
Respondents came from a wide range of positions in finance, including marketing (32%), sales/business development (16%), administration (13%), and IT/operations (11%).
To some degree, every contemporary organization has data challenges — a fact that may be especially true in financial services. Or at least that’s the perception from the inside, as only 2% of respondents believe that they are leading the way on this front. An additional 19% describe themselves as fast followers, with the rest identifying themselves as average (31%), slightly behind (22%), or farbehind (26%).
On the one hand, this is good news since it means that there’s room for cutting-edge organizations to double down on data analytics and take the lead. On the other hand, it means that there’s a hurdle to overcome in terms of perception across the industry. If everyone at your organization believes that you’re behind the curve, that belief can become a self-fulfilling prophecy, so this perception is something worth addressing with your team. It’s also worth showing your team that they might be doing better than they think since it’s unlikely that only 19% are truly doing better than average.
When given a list of five common data challenges, respondents listed “not having the right technology” in the number one spot, closely followed by “needing more employees with expertise in data analytics.” The least pressing of the problems in the list was having enough data on hand. In other words, organizations generally have something with it.
When asked what they need to address their data challenges, respondents overwhelmingly say that more than anything they need to create a company culture that makes data-driven decisions. This makes sense because the best technology won’t be put to use if the culture of a company isn’t open to integrating it into their standard processes.
Organizations have a range of goals and priorities for data initiatives, but the most common one has to do with using data to better understand their customers, followed by reaching customers on the right channels. This likely means that companies are looking for a way to quickly visualize data about their customers and then couple that data with the ability to target specific customers with the most appropriate message.
On this front, financial services companies are mostly using feedback surveys to gather information about their customers.
Given that so few organizations use data to create personalized experiences, it’s little surprise that respondents don’t feel particularly confident about their abilities here, with more than 70% of respondents saying they’re doing just OK or worse. In fact, only 6% say they’re doing well.
This leaves plenty of room for organizations to find the right marketing strategy, technology, and partner in order to take the lead when it comes to using data to drive marketing and product decisions.
What might this look like? Organizations that offer the ability for customers to aggregate their accounts in one place have the opportunity to then use that data to see a 360-degree view of their customers’ financial lives. With the right marketing technology, these organizations can then use that data to create hyper-personalized solutions geared toward improving the financial wellness of each customer. For example, say that you’re a marketer at a financial services company, and you can see that one of your customers has a mortgage with a competitor. The right technology enables you to create retargeted ads that show this user that they can refinance with you and get a lower mortgage rate. In this way, you start to rapidly outperform the market as you meet the needs of your customer, further cementing a long and mutually prosperous relationship.
78% of respondents listed targeted campaigns as one of their organization’s top initiatives to improve user experience and engagement.
It’s no wonder then that 78% of respondents listed targeted campaigns as one of their organization’s top initiatives to improve user experience and engagement.
What’s particularly interesting here is that even though data-driven initiatives can bring enormous benefits, they’re still a major point of tension within financial services companies. For example, 62% of respondents say that these initiatives produce conflict between shareholders, while only 33% say the same about AI-driven initiatives and only 24% say the same about mobile banking initiatives.
It’s clear that many leaders in financial services companies still don’t understand the value of data — especially since, as we mentioned in the introduction, AI-driven initiatives cannot work without clean and structured data.
Even though many financial services companies are lagging when it comes to data initiatives, they almost uniformly agree that AI is important to the future of their organization, with 88% saying it’s either very important (54%) or somewhat important (34%).
That said, only 6% of respondents say that their organization is very capable on this front and has already implemented these initiatives. Somewhat surprisingly, given how many people say that AI is important to them, nearly a third (29%) say that they haven’t even started conversations on the topic.
A little more than a third of respondents say they’re optimistic about their organization’s ability to develop their own AI systems, with two thirds saying they’re feeling neutral to pessimistic.
In other words, most organizations recognize that they will likely have to rely on third parties to provide data-driven AI technology such as chatbots, advanced data analytics, and personalized financial automation. This is ultimately good news since it means that bankers can focus on what they do best while partnering with financial technology companies that do what they do best. These partnerships represent the future of banking — the evolution of a legacy approach to an AI-driven approach.
Personal financial management (PFM) is a digital tool that in many cases didn’t reach widespread adoption. More than anything, this is because people don’t want to spend their weekend managing money. Instead, they want a system that can do that work for them. They’re looking for automation: Personalized financial automation. This is the key to empowering financial strength at scale.
Our survey reveals that organizations are overwhelmingly committed to improving their customers’ financial strength, with only 1% of respondents saying that it’s not important to them and 94% saying it’s either very important (61%) or somewhat important (32%) to their growth strategy. It’s clear that there’s broad agreement here about the fundamental purpose of banking.
How do financial institutions act on this strategy? Currently, they’re helping people become financially strong by offering mortgage payment relief (54%), overdraft relief (53%), digital spending analysis (41%), automated financial tips (35%), and guidance about increasing income (34%).
Of course, relief is only good to the extent it is immediately available since it is, by definition, temporary. The true value of technology such as artificial intelligence and advanced analytics lies in its ability to create long-lasting impact and make an ongoing difference in enabling financial security. On this front, respondents anticipate that artificial intelligence will help customers be more educated, call in less often with transactional issues, and get automated financial guidance — all of which result in improved financial strength across the board.
Similar to our findings about data initiatives, we found that a minority of respondents (only 5%) say that they are leading the way when it comes to giving customers automated financial guidance.
Again, this represents an enormous opportunity in financial services. If you want to lead the way when it comes to empowering financial strength with your customers, offering personalized financial automation — built on a foundation of data-driven AI — is the clear way to go.
5% of respondents say that they are leading the way when it comes to financial guidance.
At first glance, this data might seem to reveal an industry that is hopelessly behind the times when it comes to data, AI, and personalized financial automation. After all, respondents overwhelmingly feel that they are performing below average.
However, there is good news: Financial services companies don’t have to do all of this heavy lifting alone. Banks, credit unions, and fintech companies can collaborate with each other to quickly and effectively create the solutions that customers are demanding. No one has to invent solutions from scratch. We can all work together to make finances as they should be.
The way forward will require that banks, credit unions, and fintech companies make at least three key changes.
With the data from this ultimate guide and by following these three steps, you’ll be well on your way to implementing the right solution around data, AI, and personalized financial automation.