Smaller Banks Can Take on Megabanks with AI and Predictive Modeling

Whether you’re a regional bank, a community bank, or a credit union, managing your data infrastructure is a tough job. Margins are tight and running legacy systems requires constant vigilance. Finding the time and resources to exploit advancements in artificial intelligence isn’t easy. And there’s so much noise around big data it’s hard to tell where the marketing jargon ends and reality begins, so getting organizational buy-in can be a big challenge.

But smaller banks don’t have a choice. It’s a matter of survival. National banks are winning the next generation of customers and big data is a big advantage. 

“Banks really no longer have a choice of whether to get into analytics… now customer interaction is moving to the digital space across all demographics, taking more human interaction out of the equation. Analytics is the only way you can hope to personalize and influence favorable outcomes.” - ABA Banking Journal, Big Data and Predictive Analytics: A Big Deal, Indeed

There’s good news for all sizes of bank, however. The tools needed to conduct advanced analytics projects like predictive modeling are more accessible and flexible than ever. This means banks can target their analytics budget where it matters most, and avoid sinking money into systems that don’t deliver value. And they can do it on their own, or work with specialist consultants in targeted engagements.


Data Science: Rules of Engagement

We recommend five principles for regional banks and credit unions approaching advanced analytics projects:

  1. Define clear objectives. According to Gartner, 55 percent of big data projects are never finished. The problem isn’t effort or data quality. Rather, the biggest challenge is setting clear goals linked to demonstrable value. 
  2. Take an incremental approach. Making massive investments in staff and software isn’t necessary. In fact, you’ll get better results if you focus on one opportunity at a time. Then take what you learn and build on it.
  3. Think like a scientist. Hypothesize, experiment, learn, and repeat. A deductive scientific method will keep you focused and deliver results you can build on.
  4. Prioritize inquiry around customer needs. Customer commitment already sets smaller banks apart from national players. Lean into your advantage.
  5. Be prepared for and embrace surprises. A well-structured and objective analytics investigation will overcome confirmation bias to reveal insights you may not have expected. 

“Even if the bank determines it can only spend a small amount, that’s better than doing nothing.” - American Banker, Why Small Banks Need Big Data


Next Level Analytics for SMALLER Banks

Community banks already know well how to use data, whether for business analysis, segmenting, or risk management. Advancements in artificial intelligence make another level of insight possible, making predictions at the individual customer level more useful than ever before. 

  • Predict what customers need. Product cross-selling is nothing new to banks. But predicting what customers actually need, and delivering it, is possible now with behavioral analysis.
  • Build better online experiences. Whether optimizing your website experience or maximizing the effectiveness of online banking, it’s easier than ever to gather granular data that can make a difference.
  • Combine data sources to reveal the complete customer journey. Linking up CRM, contact center, and web data sources can give you a complete picture of the customer experience to identify points of strength and weakness.
  • Identify which customers might leave. Customer behavior analytics can help you predict, and respond to, potential challenges to retention.
  • Understand geography. Tracking interaction by location can tell you a lot about where your customers do business – and where you need to be. 
“National banks are now winning an outsized share of primary checking purchasers, especially among Millennials. Yet the Millennials at national banks also turn out to be more open to switching banks, which creates an opportunity for other banks that can understand and target the best parts of the segment, and then prioritize the necessary digital, marketing and other investments.” - Novantas, 2016 Omni-Channel Shopper Survey

The key to success for small banks is to set clear objectives for using big data and artificial intelligence. But be prepared and flexible enough to go where the data takes you.

Want to learn more about how incremental approaches to data science can work in your organization? Drop us a line at