AI and Automation in Community Banking

Just more than a year after OpenAI released its Large Language Model (LLM) chatbot ChatGPT and the rapid expansion of AI in general, many in the banking industry are looking at how they can join the AI race. However, the desire to jump into AI without a clear plan or understanding of what AI platforms and solutions truly have to offer can lead financial institutions down a risky path.

“I’m a strong proponent of moving quickly to evaluate and leverage the emerging AI platforms and solutions as they make sense for an organization,” said Mike Neale, PRI’s Director of System Evaluation. “But as with any technology, there should be a well thought out process and governance functions to manage the risk, expenditure, and desired outcomes.”

Neale said that platforms such as ChatGPT hold great promise for a wide variety of ways to help an organization achieve improved results. For example, an LLM can help employees quickly and efficiently produce customer and internal communications consistent with the organization’s style. But without appropriate oversight and some guardrails, it also can produce content that is more than a little problematic.

“Many know the story of the lawyer who used an LLM to provide a legal brief but failed to fact check the output,” Neale said. “It turned out that all the reference cases were made up. The causes for these kinds of errors vary, but the bottom line is that it’s important to understand what kind of content is used to train the LLM. If the model uses public internet sites, it may be consuming content of questionable accuracy.”

When determining whether AI provides the right set of tools to bring into the organization, an FI should consider the following:

•  Think of AI as raw material, not an end product. In the Financial Brand article Lessons from Ally’s Experiment with GenAI Marketing: Commit, But Carefully, Sathish Muthukrishnan, Ally’s chief information, data and digital officer, suggests that it’s helpful to think of ChatGPT and its ilk in terms of raw material, rather than an end product.

“He explains that many of the ingredients come from within the organization, in the form of data, talent, and the willingness to invest time in experimentation. Anyone who thinks of GenAI as a kind of vending machine where you instantly get perfect answers ‘will be proved wrong,’ according to Muthukrishnan.”

• Learn how to ask the right questions. It’s important to understand that getting the desired output from an LLM is a matter of asking the right questions. Commonly known as “prompting,” articles abound on how to get an LLM to respond best to the base question. Khizer Abbas, growth marketing specialist who focuses on AI, says that 90% of ChatGPT’s more than 180 million users are not maximizing the tool. He posts frameworks like The Ultimate Cheat Sheet to Write Perfect ChatGPT Prompts to help people master prompting to increase their successful answer rate.

“In order to gain the efficiencies we seek, it will be important to make sure staff using the LLMs are up to speed on the topic of prompting,” Neale said.

• Hire talent with AI skills. Beyond ChatGPT, a host of other generative AI platforms have exploded over the past year that can deliver remarkable capabilities in graphics, audio, video and more. Neale says he is confident that AI will impact community banks across the organization from marketing to loan underwriting, from operations and IT to customer experience, and from HR to the teller line. With that in mind, he suggests that it is critical that financial institutions begin sourcing leadership positions focused on AI and consider AI skills as a part of all talent acquisition. In addition, firms can train employees internally using widely available free content or partnering with experts.

In The Financial Brand article An Inside Look at Ally Bank’s Measured Roll-out of GenAI, Microsoft’s Managing Director of Data and Artificial Intelligence Priya Gore said that AI offers an opportunity throughout an organization’s structure.

“Certainly, recruiting and continuously developing AI talent is increasingly vital to stay competitive. But rather than focus on the risk of smaller players losing out, we view this as an inflection point as capabilities are actually democratized and cascade across organizations through partners. Firms are launching AI centers of excellence, mandating cross-training rotations, earmarking professional development budgets, and standardizing organizational tools’ smooth adoption.”

• Build a good governance plan. In addition to a focus on talent, build a governance plan that strikes the proper balance between risk and reward. Regulatory bodies are already gathering information about AI and asking financial institutions about their AI and machine learning policies. Management should be addressing these questions now, ahead of regulation.  

Platforms such as ChatGPT hold great promise for a wide variety of ways to help an organization achieve improved results. Neale recommends focusing on existing opportunities for process improvement that could benefit from AI. By prioritizing low-hanging fruit and creating early success stories and small victories, leaders will build momentum in the organization that will set the stage for future wins.

Resources :

The Human Touch in Digital Banking: Balancing Automation and Personalization – PRI

Lessons from Ally’s Experiment with GenAI Marketing: Commit, But Carefully – The Financial Brand

An Inside Look at Ally Bank’s Measured Roll-out of GenAI – The Financial Brand

The community banking space is poised for transformation with the emergence of new technologies. – Mike Neale, LinkedIn

ChatGPT Prompt Frameworks: Unlock the Full Potential of ChatGPT – Khizer Abbas, LinkedIn

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