Bank Data and AI: An Ultimate Guide

Regardless of the specifics, today’s digital banking innovations all rely on a foundation of connectivity and data. Take cryptocurrency, chatbots, voice assistants, aggregation, mobile banking, virtual banking, and personalized financial automation. Without the foundation of data, the latest digital initiatives at financial services companies will remain limited in the experiences they offer.

For instance, imagine asking a question to a digital voice assistant that pulls from unclean and unstructured data. You might ask what your last bank transaction was, only to hear a string of random letters and numbers such as, “Cos0861--e900-WA.” The correct data is simply “Costco,” 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 understand initiatives around data at financial institutions, we partnered with The Financial Brand and surveyed leaders at a range of banks and credit unions and released the results in the Ultimate Guide to Data, AI, and Personalized Financial Automation</a >.

Perhaps surprisingly, we found that only 2% of those surveyed — which consisted of respondents from institutions ranging from less than $500 million to more than $10 billion in assets — said that they’re leading the way when it comes to data optimization.

We also discovered:

• The initiatives that produce the most conflict between stakeholders

• The biggest data challenges at organizations

• What organizations are most interested in when it comes to data

• How financial institutions are thinking about artificial intelligence (and how many already have initiatives in place).

• And much more...