‘Demystifying GenAI Applications’: Implications for Credit Unions

What are the implications of generative artificial intelligence (AI) for the future of financial services?

In a recent May session, experts from Filene Research Institute answer this question, providing a broad perspective on how the credit union industry is approaching generative AI — and how credit union leaders can adopt this technology to increase efficiency and serve their members more effectively.

You can view the recap video (scroll down on page), as well as the presentation slides. (As a reminder, the first webinar recap in this series — “The Evolution and Impact of AI in Credit Unions” — was in February.)

During May’s webinar, Filene Research Institute Executive Vice President Christie Kimbell, Filene Fellow and DePaul University Professor Lamont Black, Best Innovation Group CEO John Best, and Altura CU Solutions Developer Chris Dierker gave an overview of:

GenAI: Strategy and Culture

• Empowering Teams with AI-Driven Insights: Using AI to analyze member data for personalized services and better decision-making, embedding data scientists within key teams.
• Restructuring for Agile AI Integration: Creating cross-functional teams and an AI-focused unit in an organization chart (org chart) to swiftly implement and adapt AI solutions.
• Cultivating a Culture of Continuous Learning: Investing in training and development programs to ensure a team can effectively use evolving AI tools, emphasizing continuous learning in the organizational structure.

Four GenAI Use Cases

• Data Infrastructure for GenAI: Organizing today, empowering tomorrow, and preparing data for AI-driven success.
• Enhanced Member Experiences: Personalizing services, member account history, transaction data, and demographic information.
• Predictive Insights: Loan repayment patterns, spending habits, and member feedback.
• Operational Efficiency: Automation, loan application data, transaction logs, customer service interactions, cost reduction, operational workflows, staffing levels, and service usage metrics.
• Risk Management and Security: Fraud detection, transaction records, log-in attempts, suspicious activity reports, regulatory compliance, compliance audit logs, transaction monitoring data, and member identification records.

Future-Ready Infrastructure

• Data-Driven Decisions: Financial performance data, market trends, and member satisfaction surveys.
• Innovation and Growth: Product usage statistics, competitive analysis, and innovation project outcomes.

GenAI Experimentation, Questions and Answers, and Closing Thoughts

• Empower Your Future with Organized Data: Preparing your credit union’s data today ensures AI can deliver personalized services, enhance operational efficiency, and provide robust risk management for tomorrow.
• Tailor Your AI Approach: Whether you choose to build, buy, or configure AI solutions, there’s no one-size-fits-all approach. You should assess your credit union’s unique needs, resources, and expertise to find the best path forward.
• Leverage AI for Innovation and Growth: By strategically implementing AI, your credit union can stay competitive, improve member experiences, and drive future growth in an evolving financial landscape.

You can also access The Evolution and Impact of AI in Credit Unions, a brief outline on how credit union leaders can develop an AI strategy and begin taking practical steps toward further AI adoption (by Filene Research Institute).