Featuring insights from Ashish Garg, Co-founder and CEO of Eltropy, on the key use cases for Generative AI in Credit Unions, and the strategic considerations that leaders should bear in mind. Based on his original contribution to the Forbes Technology Council, here’s an in-depth look,
The winds of change are sweeping through industries, and the credit union and community banking world is no exception.
As the era of generative artificial intelligence (AI) unfolds, credit unions find themselves at a crossroads. The potential applications of generative AI, particularly through tools like ChatGPT, are vast, promising an era of enhanced member engagement, operational efficiency and innovation. However, they may not know where or how to get started.
In this article, I’ll explore five key use cases for generative AI in credit unions while also addressing potential roadblocks and highlighting the strategic considerations that credit union leaders should bear in mind.
Personalized Advice: Tailoring Conversations To Member Needs
Personalization can be challenging when interacting with diverse members’ needs and preferences, but credit unions can harness generative AI’s language generation capabilities to create dynamic conversations that adapt to member queries.
By employing AI models that analyze member data, such as loan histories or transaction patterns, credit unions can personalize advice and recommendations, enhancing the member experience.
According to a PYMNTS and PSCU Credit Union Innovation study, credit union members initially trust their credit unions more than banking customers do their financial institutions, but technology is rapidly narrowing the gap. By providing personalized advice through generative AI, credit unions can enhance member engagement and cultivate enduring relationships in their digital interactions.
