Jody Bhagat: President of Americas at Personetics, a global leader in data-driven personalization & CE for financial institutions.
“I’m going to do some banking today,” said no one in recent memory.
Banking as we have known it has quietly vanished from our conscious daily activities, yet its influence on our everyday life continues to grow. The successful financial institutions of the future will be the ones that can transform their extensive data collections into meaningful guidance for their customers, and not the banks that simply have the most branches or products.
Research my company conducted indicates 84% of consumers would change banks for better AI-driven financial guidance.
There remains a wide schism between customer data availability and leveraging customer information to deliver meaningful financial guidance. We call this fracture the “Intelligence Gap,” and solving it is at the heart of cognitive banking.
What Is Cognitive Banking?
The practice of cognitive banking is a return to delivering on the fundamental tenets of banking: knowing your customers and delivering personalized guidance. With the latest in artificial intelligence and analytics, these outcomes can now be delivered efficiently at scale—not just for your best customers, but for all customers.
AI in cognitive banking analyzes customer behavioral patterns to understand their financial needs, enabling the delivery of proactive guidance (full disclosure: Personetics offers this solution).
Four Steps To Implement Cognitive Banking
From my work with financial institutions across the world, I’ve determined that organizations need to follow four essential steps for building successful cognitive banking systems.
1. Build a data foundation with purpose.
It’s well understood that an effective data foundation is key to making sense of customer data and applying AI-driven insights. The challenge is that most banks are dissatisfied with their current state of data quality, readiness and availability.
An effective cognitive banking business model relies on a foundation of customer data that is cleansed, categorized and enriched to be able to derive intelligence from the data.
Fortunately, tools and techniques exist to perform data cleansing and enrichment without undergoing massive data transformation projects that typically stifle business initiatives. Large, multiyear standalone data transformation projects are inconsistent with what banks need to compete.
2. Start with high-impact use cases.
Successful cognitive banking programs start with priority use cases that achieve customer-driven outcomes. One of our clients, a regional bank in the midwestern U.S., started with three priority use cases, which led to a 35% boost in digital user participation during their first three months. These included:
• Monitoring subscription changes to notify customers about recurring charge modifications
• Providing cash flow prediction alongside automatic low-balance notification features
• Generating automated savings recommendations through spending pattern analysis
Each of these micro-journeys was a key part of delivering on their brand promise of looking out for customers.
3. Connect digital and human channels.
The cognitive banking model applies across the enterprise, not simply in the digital channel. This means that customer intelligence, regardless of where it is captured, is propagated to digital and human channels and into the marketing technology (martech) stack.
Rather than operating as parallel, independent channels, digital and human channels can reinforce each other when they have equal access to customer intelligence. Digital interactions can promote outreach to a banker when appropriate. Banker interactions can promote the bank’s digital capabilities, so customers are more confident in the self-service capabilities.
4. Evolve from insights to actions.
The most sophisticated cognitive banking implementations move beyond simply providing information to enabling immediate action. As one banking executive recently told me, “It’s not just about developing the insight, it’s about making it actionable—giving people the ability to act in the moment.”
This might include:
• Saving money through one-touch features that analyze their spending habits
• Automated debt optimization suggestions
• Proactive overdraft protection based on predicted cash flow shortfalls
When financial insights become available for immediate customer action, both engagement levels and achieved outcomes rise substantially.
Challenges And Considerations
While the benefits of cognitive banking are compelling, financial institutions must navigate key challenges to realize its potential.
A quality data foundation is the most critical hurdle. Banks that rush into AI-driven insights without addressing transaction data quality and accessibility that can be readily cleansed and enriched will likely produce subpar results. Fortunately, banks don’t need to undertake extended data transformation projects to get started with AI-driven insights.
Creating an integrated customer experience is also an important consideration. Banks that take a shortcut of having a standalone and separated experience solely for insights lose the ability to deliver contextual guidance that helps drive customer action.
Finally, measuring true business impact demands moving beyond basic engagement metrics to track meaningful outcomes like transaction volumes, account growth and customer retention. This requires sustained commitment and resources to monitor effectively.
From Utility To Essential Partner
Cognitive banking represents a complete overhaul of how banks interact with their customers rather than a singular technology initiative. The banks that successfully deploy these capabilities set themselves up as vital strategic partners versus basic service providers.
Today’s technology allows organizations to bring this vision into reality. Success in banking will go to those who apply cognitive capabilities to build stronger customer relationships than they do to process transactions. It’s about helping people achieve their financial goals, rather than simple money movement.
The question is no longer whether to adopt cognitive banking. It’s about how quickly you can close the intelligence gap between your bank and the rising expectations of today’s banking customers.
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