Smaller Banks are Falling Behind on the GenAI Curve. Here’s How to Catch Up
By Pete Chapman, chief technology officer at Grasshopper Bank
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Businesses across industries, including banking, are in a race to integrate AI, especially generative AI, into their workflows.
Recent global research by Temenos shows that 54% of financial institutions have implemented generative AI (11%) or are in the process of doing so (43%).
A closer look at those numbers reveals a troubling trend:
• 79% of banks with over $250 billion in assets and 75% of banks with $50-250 billion in assets reported having generative AI tools live or in the pipeline.
• However, among banks with less than $10 billion, only about 40% had GenAI tools in place or in planning.
Clearly, not all financial institutions are advancing at the same pace when it comes to AI adoption.
Community, regional and niche banks play a crucial role in the communities and industries they serve. To continue leading with impact, these institutions must take the opportunity to embrace GenAI to level the playing field, strengthen their capabilities, and remain competitive with larger banks.
Need to Know:
- Implementing GenAI doesn’t have to be daunting or cost-prohibitive.
- The most impactful AI applications aren’t flashy. They’re operationally embedded and designed to make both employees and clients smarter, faster and more informed.
- Small, focused AI experiments can unlock big results.
Tackle Your Bank’s Misconceptions and Start Small
Many banks, particularly smaller ones, face a handful of common pitfalls when trying to implement AI. Some banks rush in without governance or considering strategic use cases. Some fail to start at all out of fear of getting things wrong or not knowing where to begin.
Such hangups can be solved by thinking pragmatically and using tools that are already widely available.
A common misconception among financial institutions is the belief that they have to jump straight into using AI in an agentic, grand capability — such as a client chatbot — in order to make an impact on their business.
That isn’t true. Approaching AI with an “all or nothing” mindset can stall AI adoption and prevent banks from realizing practical benefits that even small, focused implementations can deliver.
A critical AI touchstone: A bank’s first experiment with AI doesn’t need to be, and shouldn’t be, a significant undertaking like a client chatbot. Instead, financial institutions should first consider how they can use generative AI internally.
Key questions your institution should ponder:
- What departments and processes could benefit from using forms of AI?
- How can employees begin to familiarize themselves with the technology?
- Where can repetitive tasks be streamlined?
- Which teams could test for research purposes and report back with insights?
Don’t Hesitate to Use Existing Solutions
Many bank employees are already using tech platforms, like Google and Microsoft, that have invested heavily in GenAI tools. Merely having employees experiment with these solutions that are at their fingertips can be a great first step in the AI journey. This strategy allows smaller financial institutions to begin their adoption of AI without having to make immediate, significant investments.
Having an employee base that’s grown familiar with AI and can use the technology to streamline the more labor-intensive banking processes is at least as impactful as unveiling a flashy, customer-facing AI tool. This will cost significantly less, too.
Start with simple use cases and readily available solutions, and build up from there.
At Grasshopper Bank we’ve found that agility, not scale, is the real differentiator.
Read more: Don’t Let Data Paranoia Hamper Your Bank’s Use of GenAI
An Example of Using AI to Solve Problems in the Trenches
There are dozens of back-office tasks across banks that can benefit from incorporating GenAI. Getting started with a small handful of processes to test AI with can lead to lasting efficiency gains.
At Grasshopper, our lending teams identified that repetitive tasks, like following up on missing documents, can be automated with AI. This process formerly took two to three hours per loan. Now it takes two to three minutes — creating better results for our team and our clients.
Our lending team has found several additional use cases for AI beyond automating follow ups, particularly with auto loans. Using AI to automate document collection and begin the internal sorting process has become a big timesaver. This has allowed the auto team to operate more efficiently.
Here’s how AI can improve auto lending: We receive many auto loan applications on weekends because consumers typically shop for cars on Fridays and Saturdays. In the past, those applications would sit until our loan operations team was back in the office on Monday to review and verify them.
GenAI does the grunt work. Recently, we’ve introduced GenAI to the know-your-customer process, using the technology to verify driver’s license information and other personal details.
This moves the application process along much faster — there are fewer weekend holdups, the loan operations team can perform quality control more quickly, and ultimately, the borrower benefits from a smoother, more efficient path from application to decision.
Critically, there are still human checkpoints throughout the auto loan review process — nothing can truly replace human decision-making. But using GenAI to reduce time-intensive tasks like sorting through driver’s license details allows our team to process a much higher volume of applications.
Read more: Five Real-World AI Applications That Will Boost Your Bank’s Operations
How to Find the Right Balance of AI and Human
Being practical also means assessing comfort levels with GenAI and finding ways to use it accordingly.
For example, a bank might be uncomfortable involving GenAI in credit decisioning, which could scare them off from using the technology at all.
Banks shouldn’t let discomfort with particular applications of AI dissuade them from using it. Instead, find a level of involvement that feels comfortable and makes sense. In this example, a bank could use AI to help collect data for loan reviews, instead of involving it in the actual decision-making.
When thinking about where to start, consider tasks that require minimal thought and a simple decision-making process. Take extracting KYC and anti-money laundering information. AI can pull such data from PDFs and scanned documents much faster than humans can. Other such tasks include standardizing and cross-checking vendor and client data, and sorting contracts and invoices.
Experimenting with automating these routine tasks builds comfort with AI with minimal risk.
Scaling Up with AI Requires a Deliberative Process
Implementing generative AI requires constant feedback. Banks should assess how much time a task takes to perform manually and compare that to how long it takes with the help of automation.
Processes that show significant time savings not only confirm that resources are being well-spent, but can also spark consideration for additional use cases.
These feedback loops also help identify areas that will not benefit from introducing generative AI. For example, at Grasshopper, through internal conversations, we’ve decided not to use GenAI in the credit decisioning process.
Why? Weighing whether or not to extend a loan or line of credit requires consistent, repeatable logic. Our teams have found that generative AI isn’t truly deterministic in this way — it might reach different decisions in very similar scenarios.
Read more:
Successful AI Adoptions Requires Strong Staff Communication
Different sections of the bank will use AI in different ways. Building a work environment that both supports AI and encourages employees to talk to one another about how they’re using it is essential. Internal feedback loops increase comfort with the technology, can lead to the discovery of new use cases, and allow for strategies to be quickly adjusted as needed.
Grasshopper’s strategy:
- Use baselines, establishing how long it takes a typical employee to complete a given task and then, on a monthly basis, measuring how using AI affects their efficiency.
- Rely on an internal Slack channel to help employees surface and share practical use cases. Examples include locating documents in shared drives, finding emails and digesting spreadsheets.
If there are meaningful gains, we know the strategy is working. If there aren’t, we’ll use that same feedback loop to identify and implement improvements.
AI is leveling the playing field inside organizations. Anyone can learn to use it, and often the most creative ideas come from those closest to the work. By giving every employee permission to experiment, banks can surface new ideas that might otherwise go unseen.
Ultimately, establishing a culture that embraces intentional and practical use of generative AI will allow small banks to better compete with large institutions.
By finding practical ways to implement the technology —remember, starting small is better than doing nothing — small banks can create lasting gains.
Read next: What Banks Can Learn from BofA’s Multi-Billion Dollar AI Bet
