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This is a summary of an episode of Pioneers, an educational podcast on AI led by our founder. Join 2,000+ business leaders and AI enthusiasts and be the first to know when new episodes go live. Subscribe to our newsletter here.
TLDR:
- Adopting AI in community banks faces challenges such as data accessibility, internal skill gaps, legacy systems integration limitations, and regulatory considerations.
- Starting with small, incremental successes and integrating RPA with AI capabilities can be an effective approach.
- AI has the potential to automate workflows that require reasoning and judgment, complementing the capabilities of RPA.
- AI can enhance efficiency, revenue generation, and customer insights, enabling targeted product offers and improved service.
- Personalization is crucial for community banks to provide tailored customer experiences and product recommendations.
- Community banks need to invest in modern infrastructure to improve data accessibility and leverage AI effectively.
- AI adoption in community banking will be an evolution rather than a revolution.
Before we dive into the key takeaways from this episode, be sure to catch the full episode here:
Introduction to Community Banking and Carter Bank
Matt Speare's journey to becoming COO of Carter Bank is as unique as the institution he helps lead. Starting his career as an attack helicopter pilot for the U.S. Army, Matt transitioned into banking, beginning as a network engineer and gradually expanding his IT skillset.
Carter Bank, while classified as a community bank, operates at the larger end of this scale. It embodies the essence of community banking - focused on deposits and loans, serving smaller geographic areas compared to national banks, maintaining close ties with its customers, and prioritizing local economic growth over expansive reach.
The Unique Position of Community Banks
Community banks occupy a distinct niche in the financial landscape. They differ from national and regional banks in several key ways:
- They serve smaller, more localized areas
- They often have a high market share in specific towns or counties
- They maintain more intimate relationships with their customers
- They're less likely to be found in large metropolitan areas
This unique position allows community banks to offer personalized services that larger institutions often struggle to match.
Opportunities for AI in Community Banking
Generative AI presents a golden opportunity for community banks to level the playing field. Matt identifies several key areas where AI could benefit Carter Bank and similar institutions:
- Deepening customer relationships through better data analysis
- Personalizing marketing offers to individual customers
- Enhancing customer experience across various touchpoints
These opportunities represent a fundamental shift in how community banks operate and interact with their customers.
Matt envisions a future where AI enhances rather than replaces the personal touch that defines community banking. From tailored marketing to improved customer experiences, AI could be the key to helping smaller institutions compete with banking giants.
Challenges in Implementing AI in Community Banks
Despite the opportunities, the path to AI implementation is not without obstacles. Matt notes three significant challenges:
- The skill gaps within the existing staff creating a steep learning curve
- The limitations of legacy systems, such as integration difficulties
- Regulatory concerns surrounding AI, particularly around fairness and transparency in areas like credit underwriting
These obstacles are particularly daunting for banks with assets under $10 billion, highlighting the need for strategic and measured approaches to AI adoption.
"Number two, the underlying systems really don't support [AI adoption] today. So, the example would be that in order for an AI model to really be able to do personalized marketing, it may have to be able to have to aggregate data from multiple different systems. And unfortunately, when you look at most banking systems, they're not open APIs.”
- Matt Speare
Strategies for AI Adoption
Matt advocates for a methodical approach to AI adoption. Rather than attempting a wholesale transformation, he suggests:
- Start small with achievable, incremental successes
- Use initial projects to familiarize staff with AI capabilities
- Gradually expand AI usage across more areas of the bank
This strategy not only manages risk but also allows for the gradual development of trust and expertise within the organization.
Gen AI and RPA: A Powerful Combination
In our discussion, Matt draws an important distinction between Robotic Process Automation (RPA) and generative AI. While RPA excels at handling routine, rule-based tasks, generative AI opens up possibilities for more complex, cognitive functions.
The synergy between these technologies could be a game-changer for community banks.
Matt sees significant potential in this combination:
- AI can handle complex decision-making, while RPA excels at repetitive tasks
- Together, they can automate more complex workflows
- This combination can enhance efficiency and improve customer service
“Intelligent automation/RPA is very effective when you can define the process (…) [and expect] a certain result to happen, and for it to happen over and over and over again.”
- Matt Speare
Personalization and Customer Insights
AI also offers powerful capabilities for improving customer experiences, especially by analyzing transaction data to identify banking relationships with other institutions.
This analysis can, for example, inform personalized product recommendations. Personalization efforts can span both in-person interactions and digital channels.
Competitive Landscape for Community Banks
The competitive landscape for community banks is evolving. Matt identifies the main threats being:
- National banks remain the primary competitors, especially when entering smaller markets.
- Fintech companies pose a growing threat, though their impact is less pronounced in smaller communities.**
Understanding this landscape is crucial for community banks to position themselves effectively.
Impact of AI on the Banking Workforce
Contrary to fears of job losses, Matt sees AI as a tool for enhancing human capabilities.
AI is likely to shift job roles rather than eliminate them. It can make employees more efficient and effective, particularly in customer-facing roles. New roles focused on managing and optimizing AI technologies may emerge.
This perspective offers a more optimistic view of AI's impact on the workforce.
Future Outlook of AI in Community Banking
Looking ahead, Matt paints a picture of a banking sector where AI and human expertise coexist and complement each other. The future of community banking lies not in replacing human judgment but in augmenting it with powerful AI tools.
As Matt emphasized in our conversation, AI is a crucial tool for community banks to remain competitive:
- It can help smaller banks operate more efficiently
- It enables improved customer service by combining AI-driven insights with personal relationships
- While change won't be overnight, banks that effectively adopt AI may gain significant advantages
The key for community banks will be finding the right balance between maintaining their traditional strengths and embracing the new possibilities brought forth by technology. This adaptation is crucial for survival in an industry that is rapidly evolving.
The community banking industry, like many others, is on a journey of reinvention. It's a journey marked by the challenge of change and the promise of innovation.
For those willing to embrace this journey, like Matt Speare and Carter Bank, the rewards are not just in the business outcomes but in the contribution to shaping a future where technology augments our ability to serve communities better, making banking more accessible, personalized, and efficient.
