As Artificial Intelligence (AI) continues to break (and fix) the internet, we all continue to circle around the same questions: Will AI eliminate the need for my job? Will robots take over? Where do humans fit into the mix?

These same questions were asked, believe it or not, back in the early 1970’s when the first ATM was built in Rockville, NY in 1969. Concerns arose about no longer needing to go to a bank teller and how the position would cease to exist shortly thereafter. But, as we all know nearly half a century later, people still feel safe and an element of familiarity when going to a bank to complete a transaction with an actual bank teller. In fact, bank teller positions reached an all-time high in 2007… who would have thought?

As financial marketers, we know that while these may be valid concerns, in our field initiatives and trends aren’t always at the front of the agenda, but it is important that they are part of the conversation.

Our previous article on Artificial Intelligence parses out the difference between weak, applied and deep learning that occurs across the different types of AI. Things that we have used as long ago as our first computer chess game, and as recently as sites learning who is in your picture based on previous pictures and tags can all be categorized as AI.

With that said, since this is yet another technology like social media or marketing automation that opens the door to potential risk and further regulation, how will this impact adoption in the future of financial services?

One of the reasons for these fears lies on the fact that the stakes are much higher in financial services than in a fun game of chess against the computer where the only consequence, if something were to go wrong, is a laugh or quick hit to the side of the PC. We are talking about people’s wealth, retirement, investments and personal worth, which of course has no ‘undo’ option if something were to go wrong.

So, with concerns like these, where does AI fit within digital financial services?

1. Cross-promotion

Although we may not be as proactive as Amazon with their recommendation search engine, we do have the ability to power a similar thinking to how we position our greatest insights and products to visitors. By properly mapping the user journey and connecting all systems that the user interacts with, AI would be able to help piece together an automated decision of what the visitor might be interested in based on what it has learned from their past actions.

This goes hand in hand with a recommended product option. By creating something like a personalized homepage for each advisor through insights gleaned from past actions, current segmentation, and invested assets, we will be able to give them exactly what information they want, whenever they want it. At the same time, we’ll be able to display exactly what we want them to look into, such as a featured fund, investment idea campaign, or updated performance documents and insights. These cross-promotions and personalized journeys aren’t quite AI to the extent of a robo-advisor (we’ll leave that topic for another day), but are definitely more attainable and help demonstrate value to the user.

2. Reporting

We all know that reporting might be one of the greatest struggles that we all face. Due to disparate data and siloed management and systems, we aren’t able to give a clear view on how our different programs and efforts are performing, as well as if certain journeys aren’t mapping correctly across platforms.

By connecting data and systems and injecting these systems with tactics and skill sets, we will more easily be able to tie the data together and act as the strategic planner by deciding what this data actually means and what we can do to act on it.

3. Deciphering Information

As a financial marketer, we know that much of our data, insights, and resources are often stored and created in both PDFs and Excel spreadsheets. It is often the case that this information needs to be pulled out of one source and combined with another to be analyzed and presented in a different format for a client or stakeholder. This is a similar issue that Bloomberg faces and because of that, they have applied AI to distinguish and pull content out between a PDF and Excel doc.


This exciting new technology is just getting its footing within the fintech industry so these applications are just what seems achievable currently. There is an entire world of opportunity this burgeoning tech could create in the fintech industry as it continues to expand over the next decade, and personally, we can’t wait to see where it will go next!

Continue onto part 2!