GenAI for Smaller Banks: Unlock Competitive Advantage with Smart Adoption

The race to integrate artificial intelligence, particularly generative AI (GenAI), is intensifying across all industries, and banking is no exception. Recent global research indicates a significant push, with over half of financial institutions either implementing GenAI or having it in their pipeline.

However, a closer look at these figures reveals a clear disparity: large banks are embracing this transformative technology at a much faster pace. While approximately 75-79% of banks with assets exceeding $50 billion are actively deploying or planning GenAI solutions, only about 40% of institutions with less than $10 billion in assets are at a similar stage. This creates a growing gap in digital capabilities.

Community, regional, and niche banks are vital to their local economies and specific industries. To maintain their leadership and impact, these institutions must proactively adopt GenAI. This isn’t just about catching up; it’s an opportunity to level the competitive playing field, enhance operational capabilities, and ensure long-term relevance against larger competitors.

Overcoming Misconceptions: Starting Small with GenAI

Many banks, especially smaller ones, face common hurdles in AI implementation. Some jump in without proper governance or clear strategic use cases, while others are paralyzed by the fear of making mistakes or simply not knowing where to begin. These challenges can be overcome through a pragmatic approach, often leveraging tools already at hand.

A prevalent misconception is that banks must dive straight into complex, customer-facing AI applications, such as a sophisticated chatbot, to see any real impact. This “all or nothing” mentality can severely stall AI adoption, preventing institutions from realizing the tangible benefits that even small, focused implementations can deliver.

Your bank’s first foray into AI doesn’t need to be a massive undertaking. Instead, prioritize internal applications of generative AI. Consider these fundamental questions:

• Which departments or processes could benefit most from AI assistance?
• How can employees begin to familiarize themselves with this technology?
• Where do repetitive tasks consume valuable time that could be streamlined?
• Which teams can pilot small AI experiments for research and provide valuable insights?

Leveraging Existing Solutions for Seamless Integration

Many banking professionals already utilize platforms like Google and Microsoft, which have made significant investments in GenAI tools. Encouraging employees to experiment with these readily available solutions can be an excellent first step in your bank’s AI journey. This strategy allows smaller financial institutions to initiate AI adoption without requiring immediate, substantial capital investments.

Cultivating an employee base familiar with AI, capable of using the technology to streamline labor-intensive banking processes, can be just as impactful as unveiling a flashy, customer-facing AI tool—and often at a significantly lower cost. Begin with simple use cases and existing solutions, then build your capabilities incrementally.

As we’ve observed at Grasshopper Bank, agility, not sheer scale, often proves to be the true differentiator.

GenAI in Action: Solving Real-World Banking Challenges

Numerous back-office tasks across banks are ripe for GenAI integration. Starting with a few key processes to test AI’s capabilities can yield significant, lasting efficiency gains. For instance, our lending teams identified that repetitive tasks, such as following up on missing documents, could be automated with AI. This process, which once took two to three hours per loan, now takes mere minutes, leading to superior outcomes for both our teams and our clients.

Beyond automating follow-ups, our lending team has discovered additional AI applications, particularly within auto loans. Using AI to automate document collection and initiate the internal sorting process has become a major time-saver, enhancing the auto team’s overall efficiency. Consider the scenario of auto loan applications received over weekends when consumers typically shop for cars. Historically, these applications would sit until Monday when our loan operations team returned to review and verify them.

Now, GenAI handles the foundational work. We’ve introduced GenAI into the Know Your Customer (KYC) process, utilizing the technology to verify driver’s license information and other personal details. This accelerates the application process considerably, reducing weekend delays and allowing the loan operations team to perform quality control more rapidly. Ultimately, borrowers benefit from a smoother, more efficient path from application submission to decision.

Critically, human checkpoints remain integral throughout the auto loan review process; no technology can truly replace human decision-making. However, by employing GenAI to reduce time-intensive tasks like sifting through driver’s license details, our team can efficiently process a significantly higher volume of applications.

Finding the Optimal Balance: AI and Human Collaboration

A practical approach to GenAI also involves assessing comfort levels and deploying the technology accordingly. For example, a bank might be hesitant about involving GenAI in credit decision-making, which could unfortunately deter them from using AI altogether. Banks should not let discomfort with specific advanced AI applications prevent them from embracing the technology where it makes sense. Instead, find a comfortable and logical level of involvement.

In the credit example, a bank could use AI to efficiently collect data for loan reviews, rather than entrusting it with the final decision. When determining where to begin, prioritize tasks requiring minimal thought and simple decision-making processes. Extracting KYC and Anti-Money Laundering (AML) information, for instance, can be done by AI from PDFs and scanned documents far faster than by humans. Other suitable tasks include standardizing and cross-checking vendor and client data, and efficiently sorting contracts and invoices.

Experimenting with automating these routine, low-risk tasks gradually builds comfort with AI.

Strategic Scaling: The Importance of Deliberative Process

Implementing generative AI requires continuous feedback and evaluation. Banks should establish baselines by measuring how long a task typically takes manually, then compare that to the time taken with AI assistance. Processes demonstrating significant time savings not only validate resource allocation but can also inspire the identification of additional valuable use cases.

These feedback loops are also crucial for identifying areas where generative AI might not be beneficial. For example, through internal discussions at Grasshopper, we’ve opted against using GenAI in the core credit decision-making process. Why? Extending a loan or line of credit demands consistent, repeatable logic. Our teams found that generative AI isn’t inherently deterministic in this manner; it could potentially arrive at different decisions in very similar scenarios, which isn’t suitable for this critical function.

Cultivating an AI-Ready Culture and Communication

Different departments within a bank will leverage AI in varied ways. It’s essential to foster a work environment that not only supports AI adoption but also encourages employees to openly discuss their experiences and discoveries. Internal feedback loops enhance comfort with the technology, facilitate the identification of novel use cases, and allow for rapid adjustments to strategies as needed.

Our strategy at Grasshopper Bank involves:

• Establishing baselines: We measure how long a typical employee takes to complete a task, then monthly assess how AI affects their efficiency.
• Utilizing an internal communication channel (e.g., Slack) to help employees surface and share practical AI use cases, such as locating documents, finding specific emails, or summarizing spreadsheets.

If we observe meaningful gains, we know our strategy is effective. If not, we use the same feedback mechanism to identify and implement necessary improvements.

AI is democratizing capabilities within organizations. Anyone can learn to use it, and often, the most creative and impactful ideas emerge from those closest to the actual work. By empowering every employee to experiment responsibly, banks can uncover new solutions that might otherwise remain undiscovered.

Ultimately, establishing a culture that embraces the intentional and practical use of generative AI will enable smaller banks to effectively compete with larger institutions. By identifying pragmatic ways to implement this technology—remembering that starting small is far better than doing nothing—small banks can achieve lasting competitive advantages and operational excellence.