Floyd Rummel III, CEO, Northern Hills Federal Credit Union
A disruptive force
Kirk Drake, president and founder of CU2.0 and author of “FinAncIal: Helping Financial Services Executives Prepare for an Artificial World,” predicts AI could prove more disruptive than the internet as it changes “our entire relationship with technology.”
For example, Drake says a financial institution could use an AI-based tool to assemble a pattern based on how a specific member handles financial matters. The member could then opt into an AI-based, personalized tool that would use that pattern to automate the ongoing process of moving money and paying bills.
Drake says the nation’s largest banks likely have a five-year head start on exploring these types of “transformative” AI applications.
That makes it more urgent for credit unions to take advantage of their ability to share data and services, as well as fintech’s need to find partners in delivering AI-based financial services.
“We’re the right ecosystem to experiment,” he says.
Assemble your AI ‘A team’
When securing leadership’s buy-in of artificial intelligence (AI), it’s critical to communicate the financial and nonfinancial benefits of this tool, according to “A Lender’s Roadmap to AI Adoption,” a white paper from Zest AI, a CUNA Strategic Services alliance provider.
That requires engaging with these “A-Team” influencers to communicate AI’s impact:
The champion. They make the business case for AI. AI-driven underwriting has multiple benefits, such as reaching new borrowers, reducing loan losses, and setting more accurate prices for better yield.
The subject matter expert. This person can explain how the model makes decisions, why applicants receive a particular risk score, the amount of control the credit union has over decisions, how quickly decisions can be made, and the degree to which decisions can or will be automated.
The regulatory compliance officer. This person can address how the model makes decisions, how to ensure models are fair, how much influence the credit union has over data used in the model, and how to explain the decisioning process to regulators to demonstrate compliance.
The head of information technology. They are mostly concerned with implementation logistics. This person can address how long it will take to implement a machine learning model, what resources are required, and the requirements for integration with a loan origination system.
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