Collaboration allowed BCU in Vernon Hills, Ill., to cost-effectively explore using an AI-based conversational chatbot to interact with members.
The $4.9 billion asset credit union worked with $4.4 billion asset STCU, Liberty Lake, Wash., and $3.5 billion asset Connexus Credit Union, Wausau, Wis., to explore the “virtual financial assistant” offered by Abe.ai in 2017.
“It’s like when you’re ready to dive off a cliff you gain confidence when you have partners standing beside you,” says BCU Senior Vice President Carey Price.
BCU introduced its chatbot in 2020 to offer “conversational banking” to members who use text messaging services and smart speakers such as the Amazon Echo.
The credit union upgraded its video banking to enhance its appeal for members who became accustomed to handling transactions without visiting a branch during the pandemic.
As a next step in its engagement strategy, BCU plans to use its conversational banking tool as the conduit for integrating data from video, telephone, smart speaker, text, and online channels.
The data will be funneled through Salesforce, which will act as the unified agent desktop (UAD) to track data about every interaction while enabling call center agents to use tools such as co-browsing to share screens with online members.
Price cites multiple benefits for credit unions that collaborate to explore AI:
“I encourage credit unions of all sizes to collaborate and lean in and explore,” Price says.
‘The quicker you get this in front of your business leaders, the better off you’re going to be.’
Suncoast Credit Union in Tampa, Fla., is using AI for exploratory data analysis. Data Analytics Director Nolan Walker says the result is “directional knowledge” about opportunities hidden within operational and member data.
The data team at the $14 billion asset credit union built a “supervised learning model” in 2020 to mitigate internal risk, for example. The model leverages machine learning to gather information from features within basic lending metrics such as delinquencies and charge-offs.
The model predicts the outcome of a given product for the following 90 days, including projected delinquencies, the likelihood a delinquent loan will become a charge-off, and the potential for a member with a delinquent loan to independently remedy the situation, which reduces net risk.
Walker notes that AI projects assigned to the data team make high demands on time and talent. When AI is not the most effective solution to the problem, this cost tends to outweigh the benefits.
Walker highlights two common barriers to using AI to explore existing data:
AI often challenges traditional ways of doing business, which makes it harder for leaders to trust that it will be applied in members’ best interest. Walker says aligning AI with strategic and business goals is crucial, as is allotting time to creatively explore the technology. “The quicker you get this in front of your business leaders and then leverage the tough questions you get from them, the better off you’re going to be.”
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